Management method and system for implementation, execution, data collection, and data analysis of a structured collection procedure which runs on a collection device

ABSTRACT

A collection device for performing a structured collection procedure may include a processor that executes program instructions communicably coupled to at least one memory. The processor can initiate a schedule of events of the structured collection procedure upon one or more entry criterions being met and segregate the at least one memory into a primary data store and a secondary data store. The processor can write structured patient data collected in accordance to the schedule of events to the secondary data store. The processor can transform a relevant portion of the structured patient data into an evaluated data object. The processor can generate a data abstraction based in part upon the evaluated data object. The processor can link the primary data store and the secondary data store with the data abstraction.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of U.S. patentapplication Ser. No. 12/974,859 filed Dec. 21, 2010, which itself was acontinuation in part of U.S. patent application Ser. No. 12/643,415filed Dec. 21, 2009 (WP 26405), which claims priority to U.S.Provisional Application Ser. No. 61/140,270 filed Dec. 23, 2008. Thepresent application is also a continuation-in-part of U.S. patentapplication Ser. No. 12/818,310 filed Jun. 18, 2010 (WP 26287), which isa continuation-in-part of U.S. patent application Ser. No. 12/643,338filed Dec. 21, 2009 (WP 25378), which also claims priority to U.S.Provisional Application Ser. No. 61/140,270 filed Dec. 23, 2008. All ofthe above applications are incorporated by reference herein in theirentirety.

TECHNICAL FIELD

Embodiments of the present invention relate generally to devicescollecting physiological information, and particularly to a system andmethod managing the implementation, execution, data collection, and dataanalysis of a structured collection procedure or protocol running on aportable, hand-held collection device.

BACKGROUND

A disease which is long lasting or which reoccurs often is definedtypically as a chronic disease. Known chronic diseases include, amongothers, depression, compulsive obsession disorder, alcoholism, asthma,autoimmune diseases (e.g. ulcerative colitis, lupus erythematosus),osteoporosis, cancer, and diabetes mellitus. Such chronic diseasesrequire chronic care management for effective long-term treatment. Afteran initial diagnosis, one of the functions of chronic care management isthen to optimize a patient's therapy of the chronic disease.

In the example of diabetes mellitus, which is characterized byhyperglycemia resulting from inadequate insulin secretion, insulinaction, or both, it is known that diabetes manifests itself differentlyin each person because of each person's unique physiology that interactswith variable health and lifestyle factors such as diet, weight, stress,illness, sleep, exercise, and medication intake. Biomarkers are patientbiologically derived indicators of biological or pathogenic processes,pharmacologic responses, events or conditions (e.g., aging, disease orillness risk, presence or progression, etc.). For example, a biomarkercan be an objective measurement of a variable related to a disease,which may serve as an indicator or predictor of that disease. In thecase of diabetes mellitus, such biomarkers include measured values forglucose, lipids, triglycerides, and the like. A biomarker can also be aset of parameters from which to infer the presence or risk of a disease,rather than a measured value of the disease itself. When properlycollected and evaluated, biomarkers can provide useful informationrelated to a medical question about the patient, as well as be used aspart of a medical assessment, as a medical control, and/or for medicaloptimization.

For diabetes, clinicians generally treat diabetic patients according topublished therapeutic guidelines such as, for example, Joslin DiabetesCenter & Joslin Clinic, Clinical Guideline for PharmacologicalManagement of Type 2 Diabetes (2007) and Joslin Diabetes Center & JoslinClinic, Clinical Guideline for Adults with Diabetes (2008). Theguidelines may specify a desired biomarker value, e.g., a fasting bloodglucose value of less than 100 mg/dl, or the clinician can specify adesired biomarker value based on the clinician's training and experiencein treating patients with diabetes. However, such guidelines do notspecify biomarker collection procedures for parameter adjustments tosupport specific therapies used in optimizing a diabetic patient'stherapy. Subsequently, diabetic patients often must measure theirglucose levels with little structure for collection and with littleregard to lifestyle factors. Such unstructured collections of glucoselevels can result in some biomarker measurements lacking interpretativecontext, thereby reducing the value of such measurements to cliniciansand other such health care providers helping patients manage theirdisease.

A patient with a chronic disease may be asked by different clinicians atvarious times to perform a number of collections in an effort todiagnose a chronic disease or to optimize therapy. However, theserequests to perform such collections according to a schedule mayoverlap, be repeats, run counter to each other and/or provide a burdenon the patient such that the patient may avoid any further attempts todiagnose their chronic disease or to optimize therapy.

In addition, if a requesting clinician does not evaluate the patientproperly to see if the schedule of requested collections is possibleand/or whether parameters for the collections are suitable and/oracceptable for the patient, having useful results from such collectionsmay be unlikely. Still further, if there has not been enough suitabledata collected to complete the requested collections, such that the datacollected is helpful towards addressing the medical question and/or theinterests of the clinician, such a request may waste the time and effortof the clinician and the patient as well as the consumables used toperform the collections. Again, such failure may discourage the patientfrom seeking further therapy advice.

Moreover, prior art collection devices used in facilitating a scheduleof collections provide limited guidance, if any at all, and simplereminders of a collection event. Such prior art devices typically needto be programmed manually by the either clinician or the patient, inwhich to govern the collection schedule. Such limited guidance andfunctionality provided by prior art collection devices can also furtherdiscourage the patient from seeking any future optimization of theirtherapy as performing another collection procedure in this manner may beviewed as being laborious by the patient, thereby leaving suchoptimization to simply guessing.

SUMMARY

It is against the above background that embodiments of the presentinvention present a system and method managing the implementation,execution, data collection, and data analysis of a prospectivestructured collection procedure running on a portable, hand-heldcollection device. Embodiments of the present invention can beimplemented on various collection devices, such as a blood glucosemeasuring device (meter) that has the capability to accept and runthereon one or more collection procedures and associatedmeter-executable scripts according to the present invention. Thesecollection procedures in one embodiment can be generated on a computeror any device capable of generating a collection procedure.

In one embodiment, a collection device for performing a structuredcollection procedure may include a processor communicably coupled to atleast one memory. Program instructions may cause the processor toperform functions when executed by the processor. The processor caninitiate a schedule of events of the structured collection procedureupon one or more entry criterions being met. The processor can segregatethe at least one memory into a primary data store and a secondary datastore. The processor can write structured patient data collected inaccordance to the schedule of events to the secondary data store of theat least one memory. The processor can transform a relevant portion ofthe structured patient data into an evaluated data object. The processorcan generate a data abstraction based at least in part upon theevaluated data object. The processor can link the primary data store andthe secondary data store with the data abstraction.

In another embodiment, a collection device for performing a structuredcollection procedure may include a processor communicably coupled to atleast one memory. Program instructions may cause the processor toperform functions when executed by the processor. The processor caninitiate a schedule of events of the structured collection procedureupon one or more entry criterions being met. The processor can segregatethe at least one memory into a primary data store and a secondary datastore. The processor can write casual patient data collected by thecollection device to the primary data store of the at least one memory.The processor can write structured patient data collected in accordanceto the schedule of events to the secondary data store of the at leastone memory. The processor can transform a relevant portion of thestructured patient data into an evaluated data object. The evaluateddata object can be a therapy result, a disease assessment, a therapyassessment, a therapy optimization and/or an adverse event. Theprocessor can write automatically the evaluated data object to thesecondary data store. The processor can apply an error checkingalgorithm to the primary data store after writing to the primary datastore. The processor can apply a different error checking algorithm tothe secondary data store after writing to the secondary data store. Theprocessor can link the primary data store and the secondary data storewith the evaluated data object.

In yet another embodiment, a method for managing data collected from astructured collection may include initiating a schedule of events of thestructured collection procedure upon one or more entry criterions beingmet. At least one memory can be segregated into a primary data store anda secondary data store. Casual patient data collected by a collectiondevice can be written to the primary data store of the at least onememory. Structured patient data collected in accordance to the scheduleof events can be written to the secondary data store of the at least onememory. A relevant portion of the structured patient data may betransformed into an evaluated data object. A data abstraction based atleast in part upon the evaluated data object may be generated. Theprimary data store and the secondary data store may be linked with thedata abstraction.

These and other advantages and features of the invention disclosedherein, will be made more apparent from the description, drawings andclaims that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description of the embodiments of the presentinvention can be best understood when read in conjunction with thefollowing drawings, where like structure is indicated with likereference numerals.

FIG. 1 is a diagram showing a chronic care management system for adiabetes patient and a clinician along with others having an interest inthe chronic care management of the patient according to an embodiment ofthe present invention.

FIGS. 2 and 2A are diagrams showing embodiments of a system suitable forimplementing a structured collection according to an embodiment of thepresent invention.

FIG. 3 shows a block diagram of a collection device embodiment accordingto the present invention.

FIG. 4 shows a depiction in tabular format of a data record embodimentcreated from using a structured collection on the collection device ofFIG. 3 according to the present invention.

FIG. 5A depicts a method of creating a structured collection procedurefor a medical use case and/or question according to an embodiment of thepresent invention.

FIGS. 5B and 5C show parameters defining a structured collectionprocedure and factors which can be considered to optimize a patient'stherapy using the structured collection procedure, respectively,according to one or more embodiments of the present invention.

FIGS. 6A, 6B, 6C, 6D, and 6E show various structured collectionprocedures embodiments defined according to the present invention.

FIG. 7A depicts a structured collection for diagnostic or therapysupport of a patient with a chronic disease according to an embodimentof the present invention.

FIG. 7B conceptually illustrates one example of a pre-defined structuredcollection procedure, and a method for customizing the pre-definedstructured collection procedure according to an embodiment of thepresent invention.

FIG. 8A shows a method for performing a structured collection procedureaccording to an embodiment of the present invention.

FIGS. 8B and 8C show a method of implementing a structured collectionprocedure via a graphical user interface provided on a collection deviceaccording to an embodiment of the present invention.

FIG. 9 shows a method for performing a structured collection procedureto obtain contextualized biomarker data from a patient according toanother embodiment of the present invention.

FIG. 10A depicts non-contextualized and contextualized data.

FIG. 10B depicts a typical collection procedure according to anembodiment of the present invention.

FIG. 11 depicts a diagram of accepted contextualized data intermingledwith non-acceptable contextualized data according to an embodiment ofthe present invention.

FIG. 12 depicts elements of software according to an embodiment of thepresent invention.

FIGS. 13 and 14 depict a collection procedure execution method accordingto an embodiment of the present invention.

FIG. 15 shows a method of providing diabetes diagnostics and therapysupport according to a use case embodiment of the present invention.

FIGS. 16, 17, and 18 depict different screen shots of a graphical userinterface according to an embodiment of the present invention.

FIGS. 19A-19D shows flow charts depicting structure collection protocolsfor optimizing the titration of insulin according to embodiments of thepresent invention.

FIG. 20 is a flow chart depicting testing methods for optimizing thetitration of insulin according to embodiments of the present invention.

FIG. 21 depicts a data flow diagram depicting the flow of data collectedfrom a structured collection procedure according to embodiments of thepresent invention.

FIG. 22 schematically depicts the flow of data collected from astructured collection procedure according to embodiments of the presentinvention.

DETAILED DESCRIPTION

The present invention will be described below relative to variousillustrative embodiments. Those skilled in the art will appreciate thatthe present invention may be implemented in a number of differentapplications and embodiments and is not specifically limited in itsapplication to the particular embodiments depicted herein. Inparticular, the present invention will be discussed below in connectionwith diabetes management via sampling blood, although those of ordinaryskill will recognize that the present invention could be modified to beused with other types of fluids or analytes besides glucose, and/oruseful in managing other chronic diseases besides diabetes.

As used herein with the various illustrated embodiments described below,the follow terms include, but are not limited to, the followingmeanings.

The term “biomarker” can mean a physiological variable measured toprovide data relevant to a patient such as for example, a blood glucosevalue, an interstitial glucose value, an HbA1c value, a heart ratemeasurement, a blood pressure measurement, lipids, triglycerides,cholesterol, and the like.

The term “contextualizing” can mean documenting and interrelatingconditions that exists or will occur surrounding a collection of aspecific biomarker measurement. Preferably, data about documenting andinterrelating conditions that exists or will occur surrounding acollection of a specific biomarker are stored together with thecollected biomarker data and are linked to it. In particular, a furtherassessment of the collected biomarker data takes into account the dataabout documenting and interrelating conditions so that not only the dataas such are evaluated but also the link between data to which it iscontextualized. The data about documenting and interrelating conditionscan include for example information about the time, food and/orexercises which occurs surrounding a collection of a specific biomarkermeasurement and/or simultaneously thereto. For example, the context of astructured collection procedure according in an embodiment to thepresent invention can be documented by utilizing entry criterion forverifying a fasting state with the user before accepting a biomarkervalue during a Basal titration optimization focused testing procedure.

The term “contextualized biomarker data” can mean the information on theinterrelated conditions in which a specific biomarker measurement wascollected combined with the measured value for the specific biomarker.In particular, the biomarker data are stored together with theinformation on the interrelated conditions under which a specificbiomarker measurement was collected and are linked thereto.

The term “criteria” can mean one or more criterions, and can be at leastone or more of a guideline(s), rule(s), characteristic(s), anddimension(s) used to judge whether one or more conditions are satisfiedor met to begin, accept, and/or end one or more procedural steps,actions, and/or values.

The term “adherence” can mean that a person following a structuredcollection procedure performs requested procedural steps appropriately.For example, the biomarker data should be measured under prescribedconditions of the structured collection procedure. If then theprescribed conditions are given for a biomarker measurement, theadherence is defined as appropriate. For examples, the prescribedconditions are time related conditions and/or exemplarily can includeeating of meals, taking a fasting sample, eating a type of meal with arequested window of time, taking a fasting sample at a requested time,sleeping a minimum amount of time, and the like. The adherence can bedefined as appropriate or not appropriate for a structured collectionprocedure or a single data point in particular of a contextualizedbiomarker data. Preferably, the adherence can be defined as appropriateor not appropriate by a range of a prescribed condition(s) or by aselectively determined prescribed condition(s). Moreover the adherencecan be calculated as a rate of adherence describing in which extent theadherence is given for a structured collection procedure or a singledata point in particular of a contextualized biomarker data.

The term “adherence event” can mean when a person executing a structuredcollection procedure fails to perform a procedural step. For example, ifa person did not collect data when requested by the collection device,the adherence is determined as not appropriate resulting in an adherenceevent. In another example, adherence criteria could be a first criterionfor the patient to fast 6 hours and a second criterion for collecting afasting bG value at a requested time. In this example, if the patientprovides the bG sampling at the requested time but fasted only 3 hoursbefore providing, then although the second adherence criterion is met,the first adherence criterion is not, and hence an adherence event forthe first criterion would occur.

The term “violation event” is a form of an adherence event in which theperson executing the structured collection (testing) procedure(protocol) does not administer a therapeutic at a recommended time, doesadminister a recommended amount, or both.

The term “adherence criterion” can include adherence and can also mean abasis for comparison (e.g., assessment) of a measured value, a valuerelated to a measured value and/or a calculated value with a definedvalue or defined range of the value wherein based on the comparison dataare accepted with approval and positive reception. Adherence criterioncan take into account time related values and/or adherence in oneembodiment, but also can take into account noise in other embodiments,and the like. Furthermore, adherence criterion can be applied tocontextualized biomarker data so that a biomarker data is accepteddepending on a comparison of the contextualized data about documentingand interrelating conditions that exists or occurs surrounding thecollection of the specific biomarker. Adherence criterion can be akin toa sanity check for a given piece of information, or group ofinformation. In one embodiment, the single data point/information orgroup of data or information is rejected if the accepted criterion isnot fulfilled. In particular, such rejected data are then not used forfurther calculations which are used to provide a therapy recommendation.Mainly the rejected data are only used to assess the adherence and/or totrigger automatically at least one further action. For example, such atriggered action prompts the user then to follow a structured collectionprocedure or a single requested action so that based on that action theadherence criterion can be fulfilled.

The term “data event request” can mean an inquiry for a collection ofdata at a single point in space-time defined by a special set ofcircumstances, for example, defined by time-related or not time-relatedevents.

The term “decentralized disease status assessment” can mean adetermination of the degree or extent of progression of a diseaseperformed by using a biomarker measurement of interest to deliver avalue without sending a sample to a laboratory for assessment.

The term “medical use case or question” can mean at least one or more ofa procedure, situation, condition, and/or question providing anuncertainty about the factuality of existence of some medical facts,combined with a concept that is not yet verified but that if true wouldexplain certain facts or phenomena. Medical use case or question can bealready deposited and stored in the system so that the user can selectbetween different medical use cases or questions. Alternatively, themedical use case or question can be defined by the user itself.

The terms “focused”, “structured”, and “episodic” are used hereininterchangeably with the term “testing” and can mean a predefinedsequence in which to conduct the testing.

The terms “software” and “program” may be used herein interchangeably.

FIG. 1 shows a chronic care management system 10 for a diabetespatient(s) 12 and a clinician(s) 14 along with others 16 having aninterest in the chronic care management of the patient 12. Patient 12,having dysglycemia, may include persons with a metabolic syndrome,pre-diabetes, type 1 diabetes, type 2 diabetes, and gestationaldiabetes. The others 16 with an interest in the patient's care mayinclude family members, friends, support groups, and religiousorganizations all of which can influence the patient's conformance withtherapy. The patient 12 may have access to a patient computer 18, suchas a home computer, which can connect to a public network 50 (wired orwireless), such as the internet, cellular network, etc., and couple to adongle, docking station, or device reader 22 for communicating with anexternal portable device, such as a portable collection device 24. Anexample of a device reader is shown in the manual “Accu-Chek® Smart PixDevice Reader User's Manual” (2008) available from Roche Diagnostics.

The collection device 24 can be essentially any portable electronicdevice that can function as an acquisition mechanism for determining andstoring digitally a biomarker value(s) according to a structuredcollection procedure, and which can function to run the structuredcollection procedure and the method of the present invention. Greaterdetails regarding various illustrated embodiments of the structuredcollection procedure are provided hereafter in later sections. In oneembodiment, the collection device 24 can be a self-monitoring bloodglucose meter 26 or a continuous glucose monitor 28. An example of ablood glucose meter is the Accu-Chek® Active meter, and the Accu-Chek®Aviva meter described in the booklet “Accu-Chek® Aviva Blood GlucoseMeter Owner's Booklet (2007), portions of which are disclosed in U.S.Pat. No. 6,645,368 B1 entitled “Meter and method of using the meter fordetermining the concentration of a component of a fluid” assigned toRoche Diagnostics Operations, Inc., which is hereby incorporated byreference. An example of a continuous glucose monitor is shown in U.S.Pat. No. 7,389,133 “Method and device for continuous monitoring of theconcentration of an analyte” (Jun. 17, 2008) assigned to RocheDiagnostics Operations, Inc., which is hereby incorporated by reference.

In addition to the collection device 24, the patient 12 can use avariety of products to manage his or her diabetes including: test strips30 carried in a vial 32 for use in the collection device 24; software 34which can operate on the patient computer 18, the collection device 24,a handheld computing device 36, such as a laptop computer, a personaldigital assistant, and/or a mobile phone; and paper tools 38. Software34 can be pre-loaded or provided either via a computer readable medium40 or over the public network 50 and loaded for operation on the patientcomputer 18, the collection device 24, the clinician computer/officeworkstation 25, and the handheld computing device 36, if desired. Instill other embodiments, the software 34 can also be integrated into thedevice reader 22 that is coupled to the computer (e.g., computers 18 or25) for operation thereon, or accessed remotely through the publicnetwork 50, such as from a server 52.

The patient 12 can also use, for certain diabetes therapies, additionaltherapy devices 42 and other devices 44. Additionally, therapy devices42 can include devices such as an ambulatory infusion pump 46, aninsulin pen 48, and a lancing device 51. An example of an ambulatoryinsulin pump 46 include but not limited thereto the Accu-Chek® Spiritpump described in the manual “Accu-Chek® Spirit Insulin Pump System PumpUser Guide” (2007) available from Roche Diabetes Care. The other devices44 can be medical devices that provide patient data such as bloodpressure, fitness devices that provide patient data such as exerciseinformation, and elder care device that provide notification to caregivers. The other devices 44 can be configured to communicate with eachother according to standards planned by Continua® Health Alliance.

The clinicians 14 for diabetes are diverse and can include e.g., nurses,nurse practitioners, physicians, endocrinologists, and other such healthcare providers. The clinician 14 typically has access to a cliniciancomputer 25, such as a clinician office computer, which can also beprovided with the software 34. A healthcare record system 27, such asMicrosoft® HealthVault™ and Google™ Health, may also be used by thepatient 12 and the clinician 14 on computers 18, 25 to exchangeinformation via the public network 50 or via other network means (LANs,WANs, VPNs, etc.), and to store information such as collection data fromthe collection device 24 to an electronic medical record of the patiente.g., EMR 53 (FIG. 2A) which can be provided to and from computer 18, 25and/or server 52.

Most patients 12 and clinicians 14 can interact over the public network50 with each other and with others having computers/servers 52. Suchothers can include the patient's employer 54, a third party payer 56,such as an insurance company who pays some or all of the patient'shealthcare expenses, a pharmacy 58 that dispenses certain diabeticconsumable items, a hospital 60, a government agency 62, which can alsobe a payer, and companies 64 providing healthcare products and servicesfor detection, prevention, diagnosis and treatment of diseases. Thepatient 12 can also grant permissions to access the patient's electronichealth record to others, such as the employer 54, the payer 56, thepharmacy 58, the hospital 60, and the government agencies 62 via thehealthcare record system 27, which can reside on the clinician computer25 and/or one or more servers 52. Reference hereafter is also made toFIG. 2.

FIG. 2 shows a system embodiment suitable for implementing a structuredcollection according to an embodiment of the present invention, which inanother embodiment can be a part of the chronic care management system10 and communicate with such components, via conventional wired orwireless communication means. The system 41 can include the cliniciancomputer 25 that is in communication with a server 52 as well as thecollection device 24. Communications between the clinician computer 25and the server 52 can be facilitated via a communication link to thepublic network 50, to a private network 66, or combinations thereof. Theprivate network 66 can be a local area network or a wide are network(wired or wireless) connecting to the public network 50 via a networkdevice 68 such as a (web) server, router, modem, hub, and the likes.

In one embodiment, the server 52 can be a central repository for aplurality of structured collection procedures (or protocols) 70 a, 70 b,70 c, 70 d, in which the details of a few exemplary structuredcollection procedures are provided in later sections. The server 52, aswell as the network device 68, can function also as a data aggregatorfor completed ones of the structured collection procedures 70 a, 70 b,70 c, 70 d. Accordingly, in such an embodiment, data of a completedcollection procedure(s) from a collection device of the patient 12 canthen be provided from the server 52 and/or network device 68 to theclinician computer 25 when requested in response to a retrieval for suchpatient data.

In one embodiment, one or more of the plurality of structured collectionprocedures 70 a, 70 b, 70 c, 70 d on the server 52 can be provided overthe public network 50, such as through a secure web interface 55 (FIG.2A, showing another embodiment of the system 41) implemented on thepatient computer 18, the clinician computer 25, and/or the collectiondevice 24. In another embodiment, the clinician computer 25 can serve asthe interface (wired or wireless) 72 between the server 52 and thecollection device 24. In still another embodiment, the structuredcollection procedures 70 a, 70 b, 70 c, 70 d, as well as software 34,may be provided on a computer readable medium 40 and loaded directed onthe patient computer 18, the clinician computer 25, and/or thecollection device 24. In still another embodiment, the structuredcollection procedures 70 a, 70 b, 70 c, 70 d may be provided pre-loaded(embedded) in memory of the collection device 24. In still otherembodiments, new/updated/modified structured collection procedures 70 a,70 b, 70 c, 70 d may be sent between the patient computer 18, theclinician computer 25, the server 52 and/or the collection device 24 viathe public network 50, the private network 66, via a direct deviceconnection (wired or wireless) 74, or combinations thereof. Accordingly,in one embodiment the external devices e.g., computer 18 and 25, can beused to establish a communication link 72, 74 between the collectiondevice 24 and still further electronic devices such as other remotePersonal Computer (PC), and/or servers such as through the publicnetwork 50, such as the Internet and/or other communication networks(e.g., LANs, WANs, VPNs, etc.), such as private network 66.

The clinician computer 25, as a conventional personalcomputer/workstation, can include a processor 76 which executesprograms, such as software 34, and such as from memory 78 and/orcomputer readable medium 40. Memory 78 can include system memory (RAM,ROM, EEPROM, etc.), and storage memory, such as hard drives and/or flashmemory (internal or external). The clinician computer 25 can alsoinclude a display driver 80 to interface a display 82 with the processor76, input/output connections 84 for connecting user interface devices86, such as a keyboard and mouse (wired or wireless), and computerreadable drives 88 for portable memory and discs, such as computerreadable medium 40. The clinician computer 25 can further includecommunication interfaces 90 for connections to the public network 50 andother devices, such as collection device 24 (wired or wireless), and abus interface 92 for connecting the above mentioned electroniccomponents to the processor 76. Reference hereafter is now made to FIG.3.

FIG. 3 is a block diagram conceptually illustrating the portablecollection device 24 depicted in FIG. 2. In the illustrated embodiment,the collection device 24 can include one or more microprocessors, suchas processor 102, which may be a central processing unit comprising atleast one more single or multi-core and cache memory, which can beconnected to a bus 104, which may include data, memory, control and/oraddress buses. The collection device 24 can include the software 34,which provides instruction codes that causes a processor 102 of thedevice to implement the methods of the present invention that arediscussed hereafter in later sections. The collection device 24 mayinclude a display interface 106 providing graphics, text, and other datafrom the bus 104 (or from a frame buffer not shown) for display on adisplay 108. The display interface 106 may be a display driver of anintegrated graphics solution that utilizes a portion of main memory 110of the collection device 24, such as random access memory (RAM) andprocessing from the processor 102 or may be a dedicated graphicprocessing unit. In another embodiment, the display interface 106 anddisplay 108 can additionally provide a touch screen interface forproviding data to the collection device 24 in a well-known manner.

Main memory 110 in one embodiment can be random access memory (RAM), andin other embodiments may include other memory such as a ROM, PROM, EPROMor EEPROM, and combinations thereof. In one embodiment, the collectiondevice 24 can include secondary memory 112, which may include, forexample, a hard disk drive 114 and/or a computer readable medium drive116 for the computer readable medium 40, representing for example, atleast one of a floppy disk drive, a magnetic tape drive, an optical diskdrive, a flash memory connector (e.g., USB connector, Firewireconnector, PC card slot), etc. The drive 116 reads from and/or writes tothe computer readable medium 40 in a well-known manner. Computerreadable medium 40, represents a floppy disk, magnetic tape, opticaldisk (CD or DVD), flash drive, PC card, etc. which is read by andwritten to by the drive 116. As will be appreciated, the computerreadable medium 40 can have stored therein the software 34 and/orstructured collection procedures 70 a, 70 b, 70 c, and 70 d as well asdata resulting from completed collections performed according to one ormore of the collection procedures 70 a, 70 b, 70 c, and 70 d.

In alternative embodiments, secondary memory 112 may include other meansfor allowing the software 34, the collection procedures 70 a, 70 b, 70c, 70 d, other computer programs or other instructions to be loaded intothe collection device 24. Such means may include, for example, aremovable storage unit 120 and an interface connector 122. Examples ofsuch removable storage units/interfaces can include a program cartridgeand cartridge interface, a removable memory chip (e.g., ROM, PROM,EPROM, EEPROM, etc.) and associated socket, and other removable storageunits 120 (e.g. hard drives) and interface connector 122 which allowsoftware and data to be transferred from the removable storage unit 120to the collection device 24.

The collection device 24 in one embodiment can include a communicationmodule 124. The communication module 124 allows software (e.g., thesoftware 34, the collection procedures 70 a, 70 b, 70 c, and 70 d) anddata (e.g., data resulting from completed collections performedaccording to one or more of the collection procedures 70 a, 70 b, 70 c,and 70 d) to be transferred between the collection device 24 and anexternal device(s) 126. Examples of communication module 124 may includeone or more of a modem, a network interface (such as an Ethernet card),a communications port (e.g., USB, Firewire, serial, parallel, etc.), aPC or PCMCIA slot and card, a wireless transceiver, and combinationsthereof. The external device(s) 126 can be the patient computer 18, theclinician computer 25, the handheld computing devices 36, such as alaptop computer, a personal digital assistance (PDA), a mobile(cellular) phone, and/or a dongle, a docking station, or device reader22. In such an embodiment, the external device 126 may provided and/orconnect to one or more of a modem, a network interface (such as anEthernet card), a communications port (e.g., USB, Firewire, serial,parallel, etc.), a PCMCIA slot and card, a wireless transceiver, andcombinations thereof for providing communication over the public network50 or private network 66, such as with the clinician computer 25 orserver 52. Software and data transferred via communication module 124can be in the form of wired or wireless signals 128, which may beelectronic, electromagnetic, optical, or other signals capable of beingsent and received by communication module 124. For example, as is known,signals 128 may be sent between communication module 124 and theexternal device(s) 126 using wire or cable, fiber optics, a phone line,a cellular phone link, an RF link, an infrared link, othercommunications channels, and combinations thereof. Specific techniquesfor connecting electronic devices through wired and/or wirelessconnections (e.g. USB and Bluetooth, respectively) are well known in theart.

In another embodiment, the collection device 24 can be used with theexternal device 132, such as provided as a handheld computer or a mobilephone, to perform actions such as prompt a patient to take an action,acquire a data event, and perform calculations on information. Anexample of a collection device combined with such an external device 126provided as a hand held computer is disclosed in U.S. patent applicationSer. No. 11/424,757 filed Jun. 16, 2006 entitled “System and method forcollecting patient information from which diabetes therapy may bedetermined,” assigned to Roche Diagnostics Operations, Inc., which ishereby incorporated by reference. Another example of a handheld computeris shown in the user guide entitled “Accu-Chek® Pocket Compass Softwarewith Bolus Calculator User Guide” (2007) available from RocheDiagnostics.

In the illustrative embodiment, the collection device 24 can provide ameasurement engine 138 for reading a biosensor 140. The biosensor 140,which in one embodiment is the disposable test strip 30 (FIG. 1), isused with the collection device 24 to receive a sample such as forexample, of capillary blood, which is exposed to an enzymatic reactionand measured by electrochemistry techniques, optical techniques, or bothby the measurement engine 138 to measure and provide a biomarker value,such as for example, a blood glucose level. An example of a disposabletest strip and measurement engine is disclosed in U.S. Patent Pub. No.2005/0016844 A1 “Reagent stripe for test strip” (Jan. 27, 2005), andassigned to Roche Diagnostics Operations, Inc., which is herebyincorporated by reference. In other embodiments, the measurement engine138 and biosensor 140 can be of a type used to provide a biomarker valuefor other types of sampled fluids or analytes besides or in addition toglucose, heart rate, blood pressure measurement, and combinationsthereof. Such an alternative embodiment is useful in embodiments wherevalues from more then one biomarker type are requested by a structuredcollection procedure according to the present invention. In stillanother embodiment, the biosensor 140 may be a sensor with an indwellingcatheter(s) or being a subcutaneous tissue fluid sampling device(s),such as when the collection device 24 is implemented as a continuousglucose monitor (CGM) in communication with an infusion device, such aspump 46 (FIG. 1). In still another embodiments, the collection device 24can be a controller implementing the software 34 and communicatingbetween the infusion device (e.g., ambulatory insulin pump 46 andelectronic insulin pen 48) and the biosensor 140.

Data, comprising at least the information collected by the biosensor140, is provided by the measurement engine 138 to the processor 102which may execute a computer program stored in memory 110 to performvarious calculations and processes using the data. For example, such acomputer program is described by U.S. patent application Ser. No.12/492,667, filed Jun. 26, 2009, titled “Method, System, and ComputerProgram Product for Providing Both an Estimated True Mean Blood GlucoseValue and Estimated Glycated Hemoglobin (HbA1C) Value from StructuredSpot Measurements Of Blood Glucose,” and assigned to Roche DiagnosticsOperations, Inc., which is hereby incorporated by reference. The datafrom the measurement engine 138 and the results of the calculation andprocesses by the processor 102 using the data is herein referred to asself-monitored data. The self-monitored data may include, but notlimited thereto, the glucose values of a patient 12, the insulin dosevalues, the insulin types, and the parameter values used by processor102 to calculate future glucose values, supplemental insulin doses, andcarbohydrate supplement amounts as well as such values, doses, andamounts. Such data along with a date-time stamp 169 for each measuredglucose value and administered insulin dose value is stored in a datafile 145 of memory 110 and/or 112. An internal clock 144 of thecollection device 24 can supply the current date and time to processor102 for such use.

The collection device 24 can further provide a user interface 146, suchas buttons, keys, a trackball, touchpad, touch screen, etc. for dataentry, program control and navigation of selections, choices and data,making information requests, and the likes. In one embodiment, the userinterface 146 can comprises one or more buttons 147, 149 for entry andnavigation of the data provided in memory 110 and/or 112. In oneembodiment, the user can use one or more of buttons 147, 149 to enter(document) contextualizing information, such as data related to theeveryday lifestyle of the patient 12 and to acknowledge that prescribedtasks are completed. Such lifestyle data may relate to food intake,medication use, energy levels, exercise, sleep, general healthconditions and overall well-being sense of the patient 12 (e.g., happy,sad, rested, stressed, tired, etc.). Such lifestyle data can be recordedinto memory 110 and/or 112 of the collection device 24 as part of theself-monitored data via navigating through a selection menu displayed ondisplay 108 using buttons 147, 149 and/or via a touch screen userinterface provided by the display 108. It is to be appreciated that theuser interface 146 can also be used to display on the display 108 theself monitored data or portions thereof, such as used by the processor102 to display measured glucose levels as well as any entered data.

In one embodiment, the collection device 24 can be switched on bypressing any one of the buttons 147, 149 or any combination thereof. Inanother embodiment, in which the biosensor 140 is a test-strip, thecollection device 24 can be automatically switched on when thetest-strip is inserted into the collection device 24 for measurement bythe measurement engine 138 of a glucose level in a sample of bloodplaced on the test-strip. In one embodiment, the collection device 24can be switched off by holding down one of the buttons 147, 149 for apre-defined period of time, or in another embodiment can be shut downautomatically after a pre-defined period of non-use of the userinterface 146.

An indicator 148 can also be connected to processor 102, and which canoperate under the control of processor 102 to emit audible, tactile(vibrations), and/or visual alerts/reminders to the patient of dailytimes for bG measurements and events, such as for example, to take ameal, of possible future hypoglycemia, and the likes. A suitable powersupply 150 is also provided to power the collection device 24 as is wellknown to make the device portable.

As mentioned above previously, the collection device 24 may bepre-loaded with the software 34 or by provided therewith via thecomputer readable medium 40 as well as received via the communicationmodule 124 by signal 128 directly or indirectly though the externaldevice 132 and/or network 50. When provided in the latter matter, thesoftware 34 when received by the processor 102 of the collection device24 is stored in main memory 110 (as illustrated) and/or secondary memory112. The software 34 contains instructions, when executed by theprocessor 102, enables the processor to perform the features/functionsof the present invention as discussed herein in later sections. Inanother embodiment, the software 34 may be stored in the computerreadable medium 40 and loaded by the processor 102 into cache memory tocause the processor 102 to perform the features/functions of theinvention as described herein. In another embodiment, the software 34 isimplemented primarily in hardware logic using, for example, hardwarecomponents such as application specific integrated circuits (ASICs).Implementation of the hardware state machine to perform thefeature/functions described herein will be apparent to persons skilledin the relevant art(s). In yet another embodiment, the invention isimplemented using a combination of both hardware and software.

In an example software embodiment of the invention, the methodsdescribed hereafter can be implemented in the C++ programming language,but could be implemented in other programs such as, but not limited to,Visual Basic, C, C#, Java or other programs available to those skilledin the art. In still other embodiment, the program 34 may be implementedusing a script language or other proprietary interpretable language usedin conjunction with an interpreter. Reference hereafter is also made toFIG. 4.

FIG. 4 depicts in tabular form a data file 145 containing data records152 of self-monitored data 154 resulting from a structured collectionprocedure according to an embodiment of the present invention. The datarecords 152 (e.g., rows) along with the self-monitoring data 154 (e.g.,various one of the columns) can also provide associated therewithcontextual information 156 (e.g., other various ones of the columns aswell as via row and column header information). Such contextualinformation 156 can be collected either automatically, such as forexample via input received automatically from the measurement engine,the biosensor, and/or any one of the other devices, or via inputreceived from the user interface which was manually enter by the patientin response to a collection request (e.g., a question displayed by theprocessor 102 on the display 108) during the structured collectionprocedure. Accordingly, as such contextual information 156 can beprovided with each data record 152 in one embodiment, such informationis readily available to a physician and no further collection of suchinformation is necessarily needed to be provided again by the patienteither manually or orally after completing the structured collectionprocedure. In another embodiment, if such contextual information 156and/or additional contextual information is collected after completionof a structured collection procedure according to the present invention,such information may be provided in the associated data file 145 and/orrecord 152 at a later time such as via one of the computers 18, 25. Suchinformation would then be associated with the self-monitored data in thedata file 145, and thus would not need to be provided again orally ormanually. Such a process in the latter embodiment may be needed in thesituation where the structured collection procedure is implemented as orpartly as a paper tool 38 which is used with a collection deviceincapable of running the software 34 implementing such a structuredcollection procedure.

It is to be appreciated that the date file 145 (or portions thereof,such as only the self-monitored data 154) can be sent/downloaded (wiredor wireless) from the collection device 24 via the communication module124 to another electronic device, such the external device 132 (PC, PDA,or cellular telephone), or via the network 50 to the clinician computer25. Clinicians can use diabetes software provided on the cliniciancomputer 25 to evaluate the received self-monitored data 154 as well asthe contextual information 156 of the patient 12 for therapy results. Anexample of some of the functions which may be incorporated into thediabetes software and which is configured for a personal computer is theAccu-Chek® 360 Diabetes Management System available from RocheDiagnostics that is disclosed in U.S. patent application Ser. No.11/999,968 filed Dec. 7, 2007, titled “METHOD AND SYSTEM FOR SETTINGTIME BLOCK,” and assigned to Roche Diagnostics Operations, Inc., whichis hereby incorporated by reference.

In one embodiment, the collection device 24 can be provided as portableblood glucose meter, which is used by the patient 12 for recordingself-monitored data comprising insulin dosage readings and spot measuredglucose levels. Examples of such bG meters as mentioned above previouslyinclude but are not limited to, the Accu-Chek® Active meter and theAccu-Chek® Aviva system both by Roche Diagnostics, Inc. which arecompatible with the Accu-Chek® 360° Diabetes management software todownload test results to a personal computer or the Accu-Chek® PocketCompass Software for downloading and communication with a PDA.Accordingly, it is to be appreciated that the collection device 24 caninclude the software and hardware necessary to process, analyze andinterpret the self monitored data in accordance with predefined flowsequences (as described below in detail) and generate an appropriatedata interpretation output. In one embodiment, the results of the dataanalysis and interpretation performed upon the stored patient data bythe collection device 24 can be displayed in the form of a report,trend-monitoring graphs, and charts to help patients manage theirphysiological condition and support patient-doctor communications. Inother embodiments, the bG data from the collection device 24 may be usedto generated reports (hardcopy or electronic) via the external device132 and/or the patient computer 18 and/or the clinician computer 25.

The collection device 24 can further provide the user and/or his or herclinician with at least one or more of the possibilities comprising: a)editing data descriptions, e.g. the title and description of a record;b) saving records at a specified location, in particular inuser-definable directories as described above; c) recalling records fordisplay; d) searching records according to different criteria (date,time, title, description etc.); e) sorting records according todifferent criteria (e.g., values of the bG level, date, time, duration,title, description, etc.); f) deleting records; g) exporting records;and/or h) performing data comparisons, modifying records, excludingrecords as is well known.

As used herein, lifestyle can be described in general as a pattern in anindividual's habits such as meals, exercise, and work schedule. Theindividual additionally may be on medications such as insulin therapy ororals that they are required to take in a periodic fashion. Influence ofsuch action on glucose is implicitly considered by the presentinvention.

It is to be appreciated that the processor 102 of the collection device24 can implement one or more structured collection procedures 70provided in memory 110 and/or 112. Each structured collection procedure70 in one embodiment can be stand-alone software, thereby providing thenecessary program instructions which when executed by the processor 102causes the processor to perform the structure collection procedure 70 aswell as other prescribed functions. In other embodiments, eachstructured collection procedure 70 can be part of the software 34, andcan be then be selectively executed by the processor 102 either viareceiving a selection from a menu list provided in the display 108 fromthe user interface 146 in one embodiment or via activation of aparticular user interface, such as a structured collection procedure runmode button (not shown) provided to the collection device 24 in anotherembodiment. It is to be appreciated that the software 34, likewise,provides the necessary program instructions which when executed by theprocessor 102 causes the processor to perform the structure collectionprocedure 70 as well as other prescribed functions of the software 34discussed herein. One suitable example of having a selectable structuredcollection procedure provided as a selectable mode of a collection meteris disclosed by in U.S. patent application Ser. No. 12/491,523, filedJun. 25, 2009, titled “Episodic Blood Glucose Monitoring System With AnInteractive Graphical User Interface And Methods Thereof,” assigned toRoche Diagnostics Operations, Inc., which is hereby incorporated byreference.

In still another embodiment, a command instruction can be sent from theclinician computer 25 and received by the processor 102 via thecommunication module 124, which places the collection device 24 in acollection mode which runs automatically the structured collectionprocedure 70. Such a command instruction may specify which of the one ormore structured collection procedures to run and/or provide a structuredcollection procedure to run. In still another embodiment, a list ofdefined medical use cases or medical questions can be presented on thedisplay 108 by the processor 102, and a particular structured collectionprocedure 70 can be automatically chosen by the processor 102 from aplurality of structured collection procedures (e.g., procedures 70 a, 70b, 70 c, and 70 d) depending on the selection of the defined medical usecases or medical questions received by the processor 102 via the userinterface 146.

In still another embodiment, after selection, the structured collectionprocedure(s) 70 can be provided through the computer readable mediume.g., 40 and loaded by the collection device 24, downloaded fromcomputer 18 or 25, the other device(s) 132, or server 52. Server 52, forexample, may be a healthcare provider or company providing suchpre-defined structured collection procedures 70 for downloadingaccording to a selected defined medical use case or question. It is tobe appreciated that the structured collection procedure(s) 70 may bedeveloped by a healthcare company (e.g. company 64) and implemented viathe public network 50 through a webpage and/or made available fordownloading on server 52, such as illustrated in FIG. 2. In still otherembodiments, notices that a new structured collection procedure 70 isavailable for use on the collection device 24 to help address aparticular use case/medical question that a user (e.g., healthcareprovider and patient) may have can be provided in any standard fashion,such for via postal letters/cards, email, text messaging, tweets, andthe likes.

In some embodiments, as mentioned above previously, a paper tool 38 canperform some of the functions provided by the diabetes software 34. Anexample of some of the functions which may be incorporated into thediabetes software 34 and which is configured as a paper tool 38 is theAccu-Chek® 360 View Blood Glucose Analysis System paper form availablefrom Roche Diagnostics also disclosed in U.S. patent application Ser.No. 12/040,458 filed Feb. 29, 2007 entitled “Device and method forassessing blood glucose control,” assigned to Roche DiagnosticOperations, Inc., which is hereby incorporated by reference.

In still another embodiment, the software 34 can be implemented on thecontinuous glucose monitor 28 (FIG. 1). In this manner, the continuousglucose monitor 28 can be used to obtain time-resolved data. Suchtime-resolved data can be useful to identify fluctuations and trendsthat would otherwise go unnoticed with spot monitoring of blood glucoselevels and standard HbA1c tests. Such as, for example, low overnightglucose levels, high blood glucose levels between meals, and earlymorning spikes in blood glucose levels as well as how diet and physicalactivity affect blood glucose along with the effect of therapy changes.

In addition to collection device 24 and software 34, clinicians 14 canprescribe other diabetes therapy devices for patients 12 such as anambulatory insulin pump 46 as well as electronically based insulin pen48 (FIG. 1). The insulin pump 46 typically includes configurationsoftware such as that disclosed in the manual “Accu-Chek® Insulin PumpConfiguration Software” also available from Disetronic Medical SystemsAG. The insulin pump 46 can record and provide insulin dosage and otherinformation, as well as the electronically based insulin pen 48, to acomputer, and thus can be used as another means for providing biomarkerdata as requested by the structured collection procedure 70 (FIG. 2)according to the present invention.

It is to be appreciated that, and as mentioned above previously, one ormore of the method steps discussed hereafter can be configured as apaper tool 38 (FIG. 1), but preferably all the method steps arefacilitated electronically on system 41 (FIG. 2) or on any electronicdevice/computer, such as collection device 24, having a processor andmemory as a program(s) residing in memory. As is known, when a computerexecutes the program, instructions codes of the program cause theprocessor of the computer to perform the method steps associatedtherewith. In still other embodiments, some or all of the method stepsdiscussed hereafter can be configured on computer readable medium 40storing instruction codes of a program that, when executed by acomputer, cause the processor of the computer to perform the methodsteps associated therewith. These method steps are now discussed ingreater detail hereafter with reference made to FIGS. 5A and 5B.

Create a Structured Collection Procedure

FIG. 5A depicts a method 200 of creating a structured collectionprocedure 70 illustrated by FIG. 5B for a medical use case or questionwhich may be implemented in any one of the above described devices 18,24, 25, 26, 28, 36, 52 as stand alone software, as part of the diabetessoftware 34 or portions there of as part of paper tool 38. In step 202,a medical use case or question, hereafter referred to generally as usecase(s), is selected and/or can be defined. It is to be appreciated thata use case may be, for example, one selected from the following medicaluse cases or questions: a desire to know the effects of eating aparticular food; a desire to know the best time to take medicationbefore and/or after with a meal; and a desire to know the effects ofexercise on bG levels. Other use cases may be questions concerningfinding a diagnosis, how best to initialize therapy for a patient,finding a determination of status of a patient disease progression,finding the best ways to optimize a patient therapy, and the like. Stillother examples can be providing such structured collection procedures 70which can be used to help address medical questions regarding fastingblood glucose, pre-prandial glucose values, postprandial glucose values,and the like. Other medical questions can be to control the biomarker ina predefined context, to optimize the biomarker in a predefined context,related to therapy onset, type of therapy, oral mono-therapy, oralcombination therapy, insulin therapy, lifestyle therapy, adherence totherapy, therapy efficacy, insulin injection or inhalation, type ofinsulin, split of insulin in basal and bolus, and the likes. Forexample, medical questions regarding oral mono-therapy and oralcombination could include those involving sulfonylureas, biguanides,thiazolidinediones, alpha-glucosidase inhibitors, meglitinides,dipeptidyl peptidase IV inhibitors, GLP-1 analogs, taspoglutide, PPARdual alpha/gamma agonists, aleglitazar. The selected use case can beassigned to a medical use case parameter 220 depicted in FIG. 5B.

In step 204, the situation or problem surrounding the selected use casecan be defined. This can be accomplished via looking at all the factorswhich may affect a change in the use case. For example, in the use caseof desiring to know how best to optimize a patient's therapy somefactors to look at may include stress, menstrual cycle, pre-dawn effect,background insulin, exercise, bolus timing with respect to a meal, basalrate, insulin sensitivity, post-prandial behavior, and the like such asshown by FIG. 5C.

In step 206, a determination can be made as to what kinds of analysiscan be used to address or shed light on the situation or the problem.Such analysis may be, for example, selected from the following:evaluating the change in fasting blood glucose (FPG) values over thecourse of the collection procedure 70, monitoring one or more particularvalue over the duration of the collection procedure 70, determining aninsulin to carbohydrate (I:C) ratio, determining insulin sensitivity,determining best time for administering a drug with respect to anothervariable, such as meal(s), and the like. In step 208, a sampling groupdetermination can be made as to which information has to be collected,such as what biomarker(s) and the context(s) in which the biomarkersshall be collected, as well as when this information needs to becollected to conduct the analysis. For example, the sampling group canbe defined as a string of data objects, each of which consists of:target type, e.g., time based which can use a target time (e.g., usedfor an alerting feature), a lower time window bound, an upper timewindow bound, etc., or data based which defines a data type (single,aggregate, or formula), the conditions for accepting the data (e.g.,none, below a value, above a value, a formula, etc.), the type ofcollection (e.g., user input, sensor, data, etc.), as well as anyreminder screen text (e.g., static, and/or dynamic in both formattingand value insertion) for each collection. The result of this process isa schedule of collection events 222 (FIG. 5B). Next in step 210, themanner in which each or a group of the schedule of collection events 222is/are to be conducted in order to be useful for addressing thesituation or problem of the selected use case is then determined. Thisresults in one or more adherence criterions 224. In addition to and/orinstead of the manner for performing a collection, the adherencecriterion(s) 224 may also be based on one or more biomarker valuesfalling into a pre-defined range or is equal to a certain pre-definedvalue. In other embodiments, the adherence criterion(s) can be a formula(s) which uses a biomarker datum or group of such data to determine ifthe resulting value falls into the pre-defined range or is equal to acertain pre-defined value.

For example, adherence criteria 224 can describe the parameters aroundthe events 237 that the patient 12 needs to perform such as tests withina certain window, fasting for a given amount of time, sleeping for agiven amount of time, exercise, low stress, not menstruating, etc. Assuch, an adherence criterion 224 can establish the context of theinformation about to be provided. Adherence criteria 224 can also beused as mentioned above previously in another context to provide anassessment of whether the data is acceptable, and when used in such acontext may be referenced to as “acceptance” criteria. For example,before a sample is taken, the adherence criteria 224 can establishwhether steps leading up to taking of the sample are accomplished. Forexample, the processor 102 in response to a request 240 displays thequestion, “Have you been fasting for the last 8 hours?”, wherein a “Yes”response received by the processor via the user interface 146 meets theadherence criterion 224 for this step. In another example, after thesample is taken, the processor 102 can assess the received data forreasonableness using other adherence (acceptance) criterion(s). Forexample, based on prior data, a fasting bG sample should be between120-180 mg/dl, but the received value was of 340 mg/dl, and thus failssuch adherence (acceptance) criteria since being out of the predefinedrange for an acceptable value. In such an example, an adherence event242 occurs wherein the processor 102 could prompt for an additionalsample. In such a case, if the re-sampling fails too (i.e., not between120-180 mg/dl), the assessment provided by the processor 102 is that thepatient 12 has not fasted, and thus the processor 102 as instructed bythe adherence criterion upon a failing of the re-sampling extendautomatically the events 237 in the schedule of events 222 accordingly.

Next in step 212, the condition(s) and context(s) in which the scheduleof events 222 is to be started and ended can be determined. This resultsin one or more entry criterions 226 and exit criterions 228 beingprovided for the schedule of events 222 as well as possibly for a groupof other schedule of events to which the schedule of events 222 belongsif providing a package of structured collection procedures, e.g.,procedures 70 a, 70 b, 70 c, and 70 d, which may run concurrently and/orsequentially one after the other.

For example, the one or more entry criterions 226 can be used todetermine whether the patient meets the conditions to use the collectionprocedure by the processor 102 checking that, for example, the patient12 meets the entry criterion 226 based on current age being in a range,HbA1c being in a range, that the patient has a particular disease, hashad the disease over a minimum period of time, has a Body Mass Index(BMI) in a range, had a Fasting Plasma Glucose (FPG) in a range, had aparticular drug sensitivity, is taking a particular drug, taking aparticular drug dosage, meets one or more prerequisites of anotherstructured collection procedure, has completed one or more of anotherstructured collection procedure, does not have one or more particularpre-conditions, e.g., pregnant, not fasting, or contraindications, e.g.,feeling ill, feverish, vomiting, etc., and combinations thereof. Entrycriterion 226 can also initiate the schedule of events 222 by aninitiation event such as a time of day, a time of week, meal, taking ameal with a time offset, exercise, and exercise with a time offset, useof a therapeutic drug, use of a therapeutic drug with time offset,physiological circumstances, biomarker range, and biomarker within apredetermined range calculated as an offset from a prior biomarkervalue. Example of a physiological circumstance can be that entrycriterion will be met to start a structured collection procedure when apre-determined number of a physiological event, e.g., hyperglycemia,hypoglycemia, a certain temperature at a certain of day, and the like,occur within a pre-defined amount of time, e.g., hours, day, weeks, etc.Accordingly, the entry criterion can be used to support the use of needto met prerequisites, indications for usage, and/or contraindicationsfor usage. For example, an entry criterion 226 could define aprerequisite condition which in order for the structure collectionprocedure 70 to run an Insulin Sensitivity optimization, the processor102 must verify first that a structured collection procedure for a Basaltitration is completed and/or has a desired result and/or as well asanother structured collection procedure for an insulin to carbohydrateratio is completed and/or has a desired result. In another example, anentry criterion 226 could define a condition which needs to meet certainindications for usage in which certain structured collection procedurescould provide segregated uses for diabetics who are Type 1 vs. Type 2 aswell as types of structure collection procedures which can be used totitrate for specific drugs. In another example, the entry criterion 226could define a condition which needs to meet certain contraindicationsfor usage, in which for example, certain structured collectionprocedures 70 will not run if the patient 12 is pregnant, sick, etc.

Examples of the one or more exit criterions 228 can be based on theprocessor 102 determining that a particular value is reached, that amean average of the primary samples values are in a range, that aparticular event(s) and/or condition(s) have or have not occurred, andcombinations thereof. Other conditions when the procedure may stop caninclude adverse events such as a hypoglycemic event, the patient issick, the patient undergoes a therapy change, etc. Additional detail mayalso by provided by the processor 102 on the display 108 to the patient12 based on what the specific exit criterion has been met. For example,in one example, if the patient 12 measures a glucose value indicatinghypoglycemia, upon exiting the procedure, the processor 102 runautomatically another alternative procedure which instructs the patient12 to ingest carbohydrates and measure his blood glucose value everyhalf an hour until the blood glucose exceeds 120 mg/dL. For thisalternative procedure, the patient 12 can also be requested by theprocessor 102 to document his meals, activity, stress, and otherrelevant details to ensure that the conditions that led to hypoglycemiaare recorded. The patient 12 may also be instructed by the processor 102to contact the clinician 14 in this and other such special cases asdeemed fit. Exit criteria can also include, for example, criterion forending such as exiting after a successful completion, or exiting afteran indeterminate completion, such as expiration of a predeterminedtimeout (logistical end), e.g., no result after n days, where n=1 to 365days, or by termination e.g., exit with unsuccessful termination due toa fail-safe. It is to be appreciated that the structured collectionprocedure 70 can also be defined to end automatically not only based onmeeting the exit criterion 228, but also when the patient 12 fails toperform a request to an acceptable level of compliance and/or when apatient physiological state has changed such that the patient is shouldnot carry out the schedule of events 222, thereby failing adherencecriteria 224, wherein the adherence event 242 is to end the structuredcollection procedure.

In step 214, guidance 230 for the user during collection can bedetermined as well as any options 232 for customizing the collection.For example, for guidance 230, the clinician 14 can use a default listof messages, or tailor messages to guide the patient 12 during executionof the collection procedure 70. As an example, one message could beprovided on a successful data acquisition (i.e., meets the adherencecriteria 224) would read, “Thank you. Your next scheduled measurement isat 1230 pm.” Alarms, such as provided by indicator 148, can also beassociated with the collection procedure 70 that remind the patient 12to take a measurement and can include a snooze functionality should thepatient 12 need additional time to conduct the measurement. The snoozefunctionality as well as other device features are discussed further inlater sections.

The result of steps 208-214 is the structured collection procedure 70being created in step 216 which associates together the use caseparameter 220, the scheduled of events 222, the adherence criterion(s)224, the entry criterion(s) 226, the exit criterion(s) 228, guidance230, and the options 232. In one embodiment, at the time of generating acollection procedure 70, the clinician 14 also generates printedmaterial that explains to the patient the following aspects (at aminimum): the purpose of the collection procedure 70 and expected idealoutcome, i.e., setting a goal for the collection procedure 70; thecollection procedure 70 design and the number of measurements needed;the entry criteria 226 that the patient 12 must satisfy beforeinitiating the collection procedure 70 and before taking each reading;and the exit criteria 228 under which the patient 12 should cease tocontinue the collection procedure 70. Such printed material as well asthe guidance 230 that can be provided during the execution of thecollection procedure 70 ensures that the patient is fully aware of whythe data collection procedure is being carried out.

Examples of the structured collection procedure 70 may be, for example,a structured collection procedure for determining aninsulin-to-carbohydrate ratio, for determining bolus timing in respectto meal start, and for determining an exercise equivalent to ingestedcarbohydrates. In step 218, the structured collection procedure 70 isthen made available for implementation and use in the system 41, such asin any of the above discussed manners mentioned with regards to FIGS. 1,2, and 3. A structured collection procedure 70 accordingly may beprovided via the above process, such as by either the medical communityor healthcare companies 64, to help the clinician 14 address and/orinvestigate a defined medical use case or problem.

FIG. 5B shows the interactions of the parameters 222, 224, 226, and 228of the structured collection procedure 70 for obtaining contextualizedbiomarker data from a diabetic patient to address a medical use caseupon which the structured collection procedure is based. As mentionedabove, the use case parameter 220 may be provided to identify themedical use case or question to which the parameters 222, 224, 226, and228 address. For example, the processor 76 of the clinician computer 25,the processor 102 of the collection device 24, and/or the server 52 mayread the medical use case parameters 220 from a plurality of structuredcollection procedures 70 a, 70 b, 70 c, 70 d (FIG. 2), such as providedon these devices and/or within the system 41, and provide a list of theavailable structured collection procedures, such as on the display 82 ofthe clinician computer 25 or the display 108 of the collection device24. Additionally, the clinician computer 25, the patient computer 18,and/or the server 52 can use the medical use case parameter 220 forlocating/sorting/filtering such structured collection proceduresaccording to a medical use case(s).

As mentioned above, the entry criterion(s) 226 establishes therequirements for initiating the structured collection procedure 70 toobtain patient data which includes biomarker data, particularly,collected in a predefined context. In one embodiment, the processor 102of the collection device 24 can use the entry criterion(s) 226 todetermine when an associated structured collection procedure 70 isappropriate for the patient's physiological context and to ensure thatall of the necessary inputs to the associated structured collectionprocedure have been established. Therefore, it is to be appreciated thatthe start date and/time of a structured collection procedure maydynamically change automatically by the processor 102 of the collectiondevice 24 if the predefined condition(s) of the entry criterion(s) 226is not satisfied. Accordingly, until the entry criterion 226 issatisfied, the start date and/time of the associated structuredcollection procedure 70 can be at some unknown time in the future.

For example, in one embodiment, a structured collection procedure 70 canbe chosen automatically by the processor 102 from a plurality ofstructured collection procedures 70 a, 70 b, 70 c, 70 d, such asprovided in memory 110 of the collection device 24, memory of thecomputer 18, 25 and/or from server 52, based on satisfying thecondition(s) of a defined entry criterion 226 for an associatedstructured collection procedure. For example, in one embodiment, a firststructured collection procedure, such as procedure 70 d, is useful forshowing trends in blood glucose levels (“bG Level Trending”). Therefore,an entry criterion 226 for the first structured collection procedure 70d may be for the patient to have a bG level mean which has elevated overa defined period (e.g., a past number of days, weeks, and months fromthe current date) above a certain pre-defined rate. For a secondstructured collection procedure, such as procedure 70 a, its entrycriteria 226 may require a particular number of bG measurement for apre-breakfast measurement over a defined period (e.g., a past number ofdays, weeks, months, from the current date) being below a pre-defined bGvalue. In such an example, the processor 102 upon start up in oneembodiment when commanded, such as via input received via the userinterface in another embodiment, or at a scheduled time as programmed bythe software 34 in still another embodiment, can run through the variousentry criteria 226 provided by the various structured collectionprocedures 70 a and 70 d that are, for example, provided in memory 110of the collection device 24 and determine whether the statedcondition(s) for the entry criteria 226 of a particular structuredcollection procedure 70 is satisfied. In this example, the processor 102determines that the historical data from past measurements in memory 110indicate that the patient's bG level mean has been elevating, and thatthe entry criterion 226 for the first collection procedure 70 d has beenmet, but not the entry criteria for the second collection procedure 70a. In this example, the processor 102 then automatically selects andstarts the first structured collection procedure 70 d based on theabove-mentioned analysis.

It is also to be appreciated that the use of the entry criterion 226 canhelp to reduce the misallocation of medical expenses by assuring thatthe indications of use for the structured collection procedure 70 havebeen met before starting the schedule of collection events 222. Theentry criterion 226 as well can help assure that any requests to performmultiple structured collection procedures do not overlap ifincompatible, are not unnecessary repeats of each other, or provide asignificant burden on the patient. In this manner, many of the notedproblems in which a patient may avoid any further attempts to diagnosetheir chronic disease or to optimize therapy can be both addressed andavoided automatically by the processor 102 of the collection device 24via use of the entry criterion 226.

As shown by FIG. 5B, the entry criteria 226 can include context specificentry criterion 234, procedure specific entry criterion 236, andcombination thereof. Examples of context specific entry criterion 234can include one or more variables to identify meals, low blood glucoseevents, insulin type and dosage, stress, and the like. In anotherexample, the context specific entry criterion 234 can be defined such asin the form of a specific question(s), to which the processor 102requires a specific answer to be received from patient via input fromthe user interface 146. For example, the processor 102 in executing theentry criterion 226 may display on the display 108 the question ofwhether the patient is willing and able to perform the structuredcollection procedure 70 over the required period. If the patientresponses affirmatively via the user interface 146, then the entrycriterion 226 has been satisfied and the processor 102 continuesautomatically with performing the collection events 237 according to thetheir associated timing as defined in the structured collectionprocedure 70. If the patient responses in the negative to the displayedquestion, then the processor 102 will not continue with the structuredcollection procedure 70, and may for example, re-schedule the asking ofsuch a question to a future time, such as if designated by an optionsparameter.

Examples of procedure specific entry criterion 236 can include one ormore variables to identify disease state, disease status, selectedtherapy, parameter prerequisites, insulin to carbohydrate ratio prior totesting insulin sensitivity, incompatible collection procedures, and thelike. The procedure specific entry criterion 236 can be defined suchthat the processor 102 will continue automatically with the structuredcollection procedure 70 with one of three initiators—the patient 12, theclinician 14, or data, e.g., if the condition(s) of the entry criterion226 is satisfied. For example, the procedure specific entry criterion236 can be satisfy if the clinician 14 has prescribed the structuredcollection procedure 70, such as via an authorized user entering via theuser interface 146 a valid password to unlock the particular structuredcollection procedure for use, in one embodiment. In another embodiment,the clinician 14 can send the password or an authorization code fromclinician computer 25 and/or server 52 to the collection device 24 whichprescribes (authorizes) the collection procedure 70 for use by thepatient 12 on the collection device 24. It is to be appreciated that oneor more structured collection procedure 70 can be provided in memory 110of the collection device 24 which cannot be used by the patient 12, andwhich can be also hidden from being viewed on the display 108, such asin a selection list, by the patient until authorized by the clinician14.

The procedure specific entry criterion 236 can be satisfy by a user forexample, by the user selecting a particular structured collectionprocedure 70 from a listing of structured collection procedures 70 a, 70b, 70 c, 70 d provided on the display 108. An example of a datainitiated procedure for criterion 236 would be that a biomarkermeasurement(s) provided to the processor 102 indicates a certaincondition which must have occurred or be present in order for the entrycriteria 226 for the particular structured collection procedure to besatisfied. Such a condition, for example, can be the occurrence of asingle event, such as a severe hypoglycemic event, or a series ofevents, such as hypoglycemic events within a given, a predetermined timeframe, such as in 24 hours from a start time, in one week from a starttime, etc, a calendar date-time, and the like.

Accordingly, the entry criteria 226 can be a single criterion ormultiple criteria that establish context and/or condition of thepatient's physiology that are relevant to the medical use case beingaddressed by the structured collection procedure 70. In anotherembodiment, the entry criteria 226 can be assessed after patient datahas been collected, such as, on historical patient data.

The schedule of events 222 specifies one or more events 237 which eachcomprises at least one or more variables defining a performance time238, the guidance 230 to perform the event, requests 240 for patientactions, which may include a request for information from the patientand/or a request for collection of at least one type of biomarker datafrom the patient, and combinations thereof. For performance time 238,the schedule of events 222 can specify timing of each event 237, such asfor a biomarker sampling at a particular time on three consecutive workdays, or one sample at time of wake-up, one sample thirty minutes later,and another sample one hour later.

The guidance 230 for each event 237 and for any criteria 224, 226, 228may include, for example, providing electronic reminders (acoustic,visual) to start, end and/or wake up at a particular time, to perform abG collection at a particular time, to ingest a particular meal orfood(s) at a particular time, to perform a certain exercise(s) at aparticular time, take medication at a particular time, and the like.Guidance 230 may also include information, questions and requests torecord particular information about physiology, health, sense ofwell-being, etc., at a particular time, suggestion to improve compliancywith the collection procedure, encouragement, and positive/negativefeedback.

It is to be appreciated that the events 237 define all the steps thatare necessary to be preformed in advance of as well as after a biomarkersampling according to a request 240, such that a reproducible set ofcircumstances, i.e., context before and/or after the sampling, iscreated in the biomarker data for the biomarker sampling. Examples ofsuch biomarker data, in the context of diabetes, include fasting bloodglucose values, pre-prandial glucose values, postprandial glucosevalues, and the like. Examples of a set of circumstances can includedata associated with the biomarker value which identifies collectedinformation in the patient data about meals, exercises, therapeuticadministration, sleep, hydration, and the likes.

Each of the events 237 in the schedule of events 222 can be time-based,event-based, or both. An event 237 can also be a start of a meal, awake-up time, start of exercise, a therapeutic administration time, arelative offset used with a prior glucose value, or a time indicatingmovement above or below a predetermined biomarker value threshold. Theevents 237 can also include any required patient actions necessary to beperformed in advance of and during biomarker sampling such thatreproducible circumstances are created at the time of biomarkersampling. This can includes one or more of meals, exercise, therapeuticadministration, sleep, hydration, and the like. Additionally, the events237 in the schedule of events 222 can be adjusted (number, types,timing, etc.), to accommodate work schedule, stressors, and the like ofthe patient 12.

As mentioned above previously, the adherence criteria 224 is used toassess qualitatively whether an event 237 performed according to theschedule of events 222 provided data which is acceptable to addressingthe medical use case upon which the structured collection procedure 70is based. In particularly, the adherence criteria 224 can providevariables and/or values used to validate data from a performed event237. For example, an adherence criterion 224 can be a check performed bythe processor 102 of the collection device 24 that a value collected inresponse to an event 237 is within a desired range, or is above, below,or at a desired value, wherein the value may be a time, a quantity, atype, and the like. The same or different adherence criteria 224 may beassociated with each of the events 237 within the schedule of events 222as well with the entry criterion(s) 226 in one embodiment, and as beingthe exit criterion 228 in another embodiment, such as illustrated byFIG. 6D (i.e., “stop exercising when bG back in target range” whichdefines both the adherence and exit criteria). In one embodiment, one ormore events 237 in the schedule of events 222 may be modified (e.g.,added, deleted, delayed, etc.) if a particular event or events fail tomet the adherence criterion 224 for the particular event or events. Inone embodiment, the failure of the adherence criterion(s) 224 cantrigger an adherence event 242. In one embodiment, upon occurrence of anadherence event 242 due to the associated adherence criterion 224 for anevent 237 not being met or satisfied, the processor 102 may require oneor more additional actions to be performed as a consequence. Forexample, the processor 102 may prompt on the display 108 additionalinformation to the patient, and/or prompt a question to determinewhether the patient 12 is sick, stressed, or unable to perform therequest e.g., eat the meal, or exercise. If the patient answers “Yes”,e.g., via the user interface 146, then as part of the adherence event242 the processor 102 can provide a delay to the schedule of event (i.e.suspend). In one embodiment, the delay can continue until the patientindicated that he or she is better in response to another questionprompter by the processor 102, such as the next day or after apredefined amount of time as also part of the adherence event. Forexample, the patient 12, as part of an event 237, is prompted by theprocessor 102 to administer a drug, but the patient is not at home, suchas for example, where his/her insulin is located. The patient 12 canselect the delay via the user interface 146, wherein the processor 102re-prompts the patient as part of the adherence event 242 after apredetermined amount of time. This delay may also have an upper limit inwhich if the schedule of events 222 is not re-started within a certainamount of the time, the structure testing procedure 70 in such acircumstance may just end. In another embodiment, another form of anadherence event 242 is a violation event, which results when the personexecuting a structure testing procedure 70 fails to make a recommendedchange in response to a request. For example, the request or part of anevent 237 may be for the patient to adjust a drug dosage from 10 U to 12U, wherein the patient answers in the negative to a question on thedisplayed on the display 108 asking if the patient will or has compliedwith such a change. In response to such a violation event, the processor102 may also send a message and/or provide a delay as previouslydiscussed above concerning the adherence event.

In another example and in one embodiment, a bG measurement must becollected before each meal in order for a structured collectionprocedure 70 to provide data that is useful in addressing the medicaluse case or question for which it was designed, such as identified bythe use case parameter 220. If, in this example, the patient fails totake a bG measurement for the lunch meal in response to a request 240for such a collection according to the schedule of the event 222, andhence the adherence criteria 224 for that event 237 fails to besatisfied, the processor 102 in response to the associated adherenceevent 242 can be programmed according to instructions in the collectionprocedure 70 to cancel all remaining events 237 in the schedule ofevents 222 for that day, mark the morning bG measurement stored in thedata file (such as data file 145 (FIG. 4) as invalid, and reschedule forthe schedule of event 222 for the next day. Other examples of furtheractions in which the processor 102 may take in response to an adherenceevent 242 may be to dynamically change the structure testing procedureby switch to a secondary schedule of event, which may be easier for thepatient to perform, provide additional events for measurements to makeup the missing data, change the exit criteria from a primary to asecondary exit criterion providing modified criterion(s), change theadherence criteria from a primary to a secondary adherence criterion,fill in the missing data for the failing event with historical data oran estimate based on the historical data, perform a particularcalculation to see if the structured collection procedure 70 can stillbe successfully performed, send a message to a particular person, suchas a clinician, of the failing event, provide a certain indication inthe associated data record 152 to either ignore or estimate the missingdata point, and the likes. In still another embodiments, the adherencecriteria 224 can be dynamically assessed, such as for example, based onone or more biomarker values and/or input received from the userinterface in response to one or more questions, via an algorithm whichdetermines whether the collected data provides a value which is usefulin addressing the medical use case or case. In this example, if thecalculated adherence value is not useful, for example, does not fallinto a desired range or meet a certain pre-define value, then furtherprocessing as defined by the resulting adherence event would then takeplace, such as any one or more of the processes discussed above.

The exit criteria 228, as mentioned previously above, establishes therequirements for exiting or completing the structured collectionprocedure 70, so that the structured collection procedure 70 hasadequate contextual data to answer the medical question addressed by thestructured collection procedure 70. The exit criterion 228 can helpincrease the efficiency of the structured collection procedure 70 byminimizing the number of required samples needed to address the medicaluse case. By “addressing”, it is meant that sufficient patient data hasbeen collected in which the clinician 14 may render an assessment to themedical use case. In other embodiments, the assessment may be indicatedby a given confidence interval. A confidence interval is a group ofdiscrete or continuous values that is statistically assigned to theparameter. The confidence interval typically includes the true value ofthe parameter at a predetermined portion of the time.

As with the entry criteria 226, the exit criteria 228 can comprise oneor more of context specific exit criterion 244, procedure specific entrycriterion 246, and combinations thereof. Examples of context specificexit criterion 244 can include one or more variables to identify mood,desired blood glucose events (i.e., blood glucose level), to indicatestress, illness, contraindications, such as for example, hyperglycemia,hypoglycemia, vomiting, a fever, and the likes. Examples of procedurespecific entry criterion 246 can include one or more variables toidentify a number of events meeting the adherence criteria, biomarkervalues being in a desired pre-determined range and/or at a desiredpre-determined value, a desired disease state, desired disease status,no change in the biomarker after a pre-determined period, or nosignificant progress over a pre-determined period to a desired biomarkervalue, and the like. It is to be appreciated that in one embodiment theexit criterion 228 can establish the condition(s) needed to be met forentry criterion 226 of a second structured collection procedure 70. Forexample, upon having a suitable Insulin-to-Carbohydrate (I:C) determinedwith a first collection procedure, such as for example, structurecollection procedure 70 b (FIG. 6B), running a structured test fordetermining the best time for administering a bolus in regards to astart of a meal, such as for example, structured testing procedure 70 c(FIG. 6C), which needs a current I:C ratio, can be conditioned such thatthe processor 102 can implement automatically a schedule of events ofthe second structured collection procedure 70 c upon meeting the exitcriterion of the first structured collection procedure 70 b at someunknown time. In other embodiment, for example, the exit criterion 228of a first structured collection procedure 70 that is being run by theprocessor 102 according to the schedule of events 222 and the entrycriterion 226 of the second structured collection procedure 70 both canbe based on the same one or more contraindications, such as mentionedabove. In such an embodiment, upon occurrence of a contraindicationbeing provided to and/or detected by the processor 102, such as via theuser interface 146 and/or the biosensor 140, respectively, which in thisexample meets the exit criterion 228 of the first structured collectionprocedure 70, the processor 102 would automatically start the scheduleof events of the second structured collection procedure 70 as the entrycriterion 226 of the second structured collection procedure 70 has alsobeen met. An example of such a second structured collection procedure 70which can be started via exiting a first structured collection procedurecan be one which has a schedule of events 222 which requests a biomarkersamplings at a routine interval, e.g., every 30 minutes, every hour,every day at a particular time, etc., until the contraindication(s)clears (e.g., biomarker value(s) reaches a desire range or value,patient 12 indicates to processor 102 via user interface 146 no longerhaving a contraindication(s), expiration of a predefined period, etc.).Such an embodiment is useful if recording the context and values of theevents after the occurrence of the contraindication(s) is a desire andin which the first collection procedure should be exited when acontraindication(s) occurs.

The exit criteria 228 can be a single criterion or multiple criteriathat establish the conditions to exit the structured collectionprocedure 70. The conditions are provided in one embodiment such toensure that adequate contextualized biomarker data has been obtained toanswer the medical question being addressed by the collection method.For example, such that a predetermined number of valid samples have beenacquired, or that the variability in the samples is below apredetermined threshold. Therefore, it is to be appreciated that the enddate and/time of the collection procedure 70 may be dynamic and bechanged automatically by the processor 102 if the predefinedcondition(s) of the exit criterion(s) 228 is not satisfied. Likewise,the conditions of the exit criterion 228 may be dynamic and be changedautomatically be the processor 102 such for example if a particularadherence criterion 224 is satisfied or not satisfied. For example, inone embodiment if adherence criterion 224 for a particular collectionevent 237 is met, then the processor 102 is instructed to use a firstexit criterion and if not met, then the processor 102 is instructed touse a second exit criterion that is different from the first exitcriterion. Accordingly, until the exit criterion 228 is satisfied, theend date and/time of the structured collection procedure 70 can be atsome unknown time in the future. In another embodiment, the exitcriteria 228 can be assessed after patient data has been collected, suchas, on historical patient data.

It is to be appreciated that the entry and exit criteria 226, 228together with the adherence criteria 224 can help to reduce both thetime to perform the structured collection procedure 70 and the expenseassociated with the collection by defining one or more of the acceptableconditions, values, structure and context needed to perform the scheduleof events 222 in an effort to make every collection event 237 countand/or reduce consumption of test strips 30 with unneeded collectionsthat do not help address the medical use case or question. Hereafterreference is made to FIGS. 6A-6E.

Structured Collection Procedure Examples

FIGS. 6A-E illustrate examples of some structured collection procedures70 a, 70 b, 70 c, and 70 d depicting their functions which can easily betranslated by one of ordinary skill in the related art into instructioncode which may be implemented on any one of the devices the abovedescribed devices 18, 24, 25, 26, 28, 36, 52. Therefore, for brevity, nodiscussion is provided in regard to pseudo-code or actual code relatingto these illustrated functions.

FIG. 6A diagrammatically illustrates an embodiment of a structuredcollection procedure 70 a used to obtain contextualized biomarker datafrom a diabetic patient. The horizontal axis shows the performance times238 of the various events 237, and the vertical axis shows adherencecriterion 224 without values. In the illustrated embodiment, the events237 can include recording information regarding a meal 248 and sleep 250in which to provide context 252 for the five-biomarker samplings 254also events 237 that are part of the schedule of events 222. In thisexample, the adherence criterion 224 for the meal 248 can be a valuewhich must be greater than a minimum value, e.g., for a carbohydrateamount. The entry criterion 226, for example, can comprise a biomarkervalue being above a particular value such as required to meetcontextualization requirements to begin the structured collectionprocedure 70 a. The exit criterion 228 as well can comprise a biomarkervalues being below a particular value such as also required to meetcontextualization requirements to end the structured collectionprocedure 70 a. Such a structured collection procedure 70 is useful forhelping to address a number of medical use cases.

GLP1 Structured Testing Procedure

For example, several epidemiological studies have confirmed thatelevated postprandial glucose (PPG) levels are a significant predictorof cardiovascular mortality and morbidity in type 2 diabetes (T2D). Forthis reason, there is a family of human once-weekly long actingglucagon-like peptide-1 (GLP 1) drugs which can be prescribed to T2Dswho show high post prandial bG values. These GLP 1 drugs are similar tothe natural hormone GLP-1 which has a key role in blood sugar regulationby stimulating insulin secretion and suppressing glucagon secretion.Therefore, a structured collection procedure 70 can be provided in oneembodiment which proposes an intensive measurement of bG values duringthe time after one or more meals over time allows therapy efficacy to beshown by means of observed reduced postprandial bG values. Based on suchobserved values, doses recommendation for a GLP 1 drug and/or whether aparticular GLP 1 drug is the right drug at all for the patient can bedetermined.

For example, the structured collection procedure 70 could be provided ona collection device 24 for when a patient has been prescribed toadminister a particular drug, e.g., a GLP 1 drug. In the case of a GLP 1drug, in which determination of drug efficacy is desired, the entrycriterion 226 for such a structured collection procedure could then bethat the patient must affirm to the processor 102 in response to aquestion displayed on the display 108 to perform the structuredcollection procedure 70 over a period of time (e.g., over the next 4 to24 weeks) and/or the processor 102 has determined that the mean PPGlevel of the patient from prior post prandial bG values over a period(e.g., week, month, etc.) are high (e.g., greater than 141 mg/dl). Stillother factors could be used as the entry criterion(s) 226, such asfasting blood glucose being more than a certain value, e.g., 126 mg/dlor less than a certain value, e.g., 240 mg/dl.

After the conditions of the entry criterion(s) 226 have been satisfiedand confirmed by the processor 102, the schedule of events 222 is thenautomatically run by the processor 102. The schedule of events 222 wouldspecify desired collection events 237 in which the processor 102 wouldautomatically prompt the patient for entering post prandial bG valuesafter breakfast, lunch, and dinner (i.e., performing a bG measurement ona sample provided to a test strip that is read by the measurement engineand provided to the processor for storing in a data record and display).As customized by the prescribing physician, the schedule of events 222could also define a collection event 237 with a performance time 238 inwhich the patient must administer the drug as well as to provide areminder of the dosage and a request 240 for confirmation from thepatient when the drug has been administered. For example, the processor102 in executing the schedule of events 222 would automatically promptthe patient to administer dosages at the times specified by thecollection events 237 in the schedule of events 222, e.g., 10 mg ofTaspoglutide on a certain day of the week, and then after a period, asecond dosage according to a second interval, e.g., after 4 weeks, then20 mg also on a certain day of the week. A collection event 237 couldalso be defined in the schedule of events 222 in which the processor 102makes a request on the display 108 for information, such as whether thepatient is feeling well, to provide an indication of energy level, toprovide an indication of size of meals consumed, and the like.

A condition(s) for the adherence of each entered post prandial bG valuecould be provided via the use of adherence criteria 224 in which anypost prandial bG value entered (i.e., measured) an amount of time beforeor after the prompting, e.g., a testing window of ±30 minutes, such ameasured value would not be accepted as a valid measurement for theschedule of events 222 by the processor 102. In one embodiment, theprocessor 102 can take further action automatically based on theadherence criteria 224 assessment preformed automatically by theprocessor 102. For example, if a bG measurement was taken before ameasurement prescribed by a collection event in the schedule of events222 and outside the defined testing window, e.g., −30 minutes before thecollection event time, the processor 102 in such a case willautomatically notify the patient that a measurement is still needed atthe prescribed time as the previous measurement was not accepted sinceoutside the testing window. Likewise, if after the testing window, e.g.,the collection event time+30 minute, the processor 102 can automaticallynotify the patient that the previous measurement was not accepted sinceoutside the testing window and provide encouragement on the display 108to the patient to make an effort take a measurement within the testingwindow.

The exit criterion 228 for such a GLP 1 structured collection procedure70 could be an indication that the mean bG value, in using a minimumamount of time (e.g., days, weeks, months, etc.), a minimum number ofaccepted measurements, or both, has reached a desire value. Likewise,the exit criterion 228 could be an indication that the mean bG value,after a maximum amount of time (e.g., days, weeks, months, etc.), amaximum number of accepted measurements, or both, has not reached adesire value. Still further, the exit criteria 228 can be other factorswhich indicate that the drug or dosage is not at all right for thepatient, such as the patient responding as having nausea and/or vomitingeach day for a minimum number of days in response to a collection eventfor such information prompted by the processor 102 on the display 108.Still other factors could be used as the exit criteria 228, such asfasting blood glucose being less than a certain value, e.g., 126 mg/dlor greater than a certain value, e.g., 240 mg/dl. The data collectedfrom such a drug base structured collection procedure 70 can then beused by a physician to make a dosage recommendation for the GLP 1 drugand/or determine whether the particular GLP 1 drug is the right drug ornot for the patient.

Another example is diagrammatically depicted by FIG. 6B which shows astructured collection procedure 70 b which has a defined medical usecase parameter 220 indicating that the procedure can be helpful fordetermining suitability of an insulin to carbohydrate (I:C) ratio. Asillustrated, the entry criterion 226 is defined as having the patientsimply acknowledge guidance 230 of selecting a fast-acting meal, to notethat the insulin dose is calculated with the current I:C ratio as wellas agreeing not to exercise, take additional food or insulin during thetesting period. For example, the processor 102 can present on thedisplay 108 such guidance 230, which the user can then acknowledge afterreading with either a “Yes” or a “No” entered via using the userinterface 146 for the desired entry choice. If the user enters “Yes”,then the entry criterion 226 is satisfied, and the processor 102automatically starts the schedule of events 222 defined in thestructured collection procedure 70 b. In another embodiment, the entrycriterion 226 may be or include satisfying a request for selecting afast-acting meal. For example, the request 240 for selection can be theprocessor 102 displaying on the display 108 a selection menu providing alisting of fast-acting meals to which input of such a selection via theuser interface 146 is needed. For example, selection of a fast-actingmeal may be made via a press of one of the buttons 147, 149 or via thetouch screen interface if provided by display 108. Such a selection canthen be stored in memory 110 of the collection device 24 such as setupdata 163 (FIG. 4) which may be part of the data file 145 (FIG. 4) forthe structured collection procedure 70 b. In an alternative embodiment,a particular fast-acting meal may be recommended by the structuredcollection procedure 70 b.

As shown, the schedule of events 222 can comprise one or more events,such as the plurality of events 237 a-k illustrated and with each havingassociated performance times 238 a-k and requests for action 240 a-k. Asshown, the requests for action 240 a-c, and 240 f-k are requests for theuser to take a bG level measurement, request 240 d is to take an insulindose, and request 240 e is to eat the fast acting meal. Also shown isthat events 238 f-k each have an adherence criterion 224, which must bemet if the data for events 238 f-k are to be recorded in the data file145. In this example, the adherence criteria 224 requires that theactions 240 f-k be completed within ∀20 minutes of their correspondingperformance times 238 f-k in order for a data record 152 recording thereceived value(s) for the corresponding event 237 f-k to count towardscompleting the collection procedure 70 b. In one embodiment, theprocessor 102 will make each of the requests 240 a-k at their associatedperformance times 238 a-k in order to obtain resulting data values e.g.,data values 256 a-k (FIG. 4) at the time the requests are performed.

For example, the processor 102 can prompt the patient 12 with a request240 a to take a bG level (biomarker) measurement at performance time 238a. The resulting measurement when received by the processor 102, such asautomatically from the measurement engine 138 after reading the teststrip (biosensor) 140 for the desired biomarker, is then recordedautomatically by the processor 102 in the date file 145 as acorresponding data value 256 a for the associated event 237 a. Foractions 240 d and 240 e, at a required time, the processor 102 canautomatically prompt the patient 12 to take the prescribed action at therequired time, and again automatically prompt the patient thereafter toconfirm that the required action has been taken, or that a predefinestatus has been achieved. A date-time stamp 169 can also be provided inthe date record 152 automatically by the processor 102 upon triggeringof the requests 240 a-k, acknowledgement of the requests 240 a-k, uponcompletion of the event 237 a-k, upon receiving a data value 256 a-k forthe event 237 a-k, and combinations thereof. Additionally, in anotherembodiment, the patient 12 can record data values 256 a-k for one ormore events 237 a-k by entering the data directly into the device 24 viathe user interface 146, wherein the processor 102 stored the entereddata values/information in the associated data record 152 for the event237 a-k, or in other embodiments can record a voice message with theinformation for later transcription into digital data. In still otherembodiments, the patient 12 can be guided by the collection device 24 torecord data for an event 237 a-k using a paper tool 38.

As mentioned previously above, each event 237 can be a recording of abiomarker value, or a request for a required patient action that isnecessary in order to create a context for the biomarker value, such asfor example, meals, exercise, therapeutic administration, and the like.In the illustrated embodiment, the context 252 for completing events 237a-c is to establish a pre-prandial baseline and a no-trend condition,and for events 237 f-k to establish a post-prandial excursion and tail.Such context 252 for these events may also be associated with thecorresponding data records 152 for each event as contextual information156 (FIG. 4). Such information is useful later when reconstructing thedata and/or when there is a desire to know the context for which thedata record was created.

It is to be appreciated that any patient action taken outside of therequired requests for patient actions 240 a-k can also be recorded bythe processor 102 but will not be considered by the processor 102 aspart of the collection procedure 70 b. Data 256 a-k for events 237 a-kthat are prospective can be identified based on a type of event, thetime of the event, the trigger of the event, and combination thereof.Each of the performance times 238 a-k can be fixed or variable based onprior data. Some of the event 237 a-k in other embodiments can also be apast, current, or a future event such as for meals, exercise, and thelike, or data values such as for hypoglycemic events, hyperglycemicevents, or data of a specific value of interest. In some embodiments,the events 237 a-k can be identified via a paper tool 38 that isprocedure based.

As also shown, the structured collection procedure 70 b will end if thecondition of the exit criterion 228 is satisfied. In this example, theexit criterion 228 is satisfied if at least three of the actions 240 f-kmet the adherence criterion 224. For example, the processor 102 mayprovide a unique identifier (e.g. an incremental count) 167 (FIG. 4) inthe data file 145 for each event 237 a-k performed and to whichsatisfied the adherence criterion 224 if required. In the illustratedembodiment of FIG. 4, events 237 a-c and 237 e-k each receive a uniqueidentifier but not event 237 d, e.g., <null>, since not satisfying anassociated adherence criteria (not shown). In addition, analysis logic258 and resulting recommendations 260 can also be provided in thestructured collection procedure 70 b which the processor 102 may applyautomatically to the data collected upon satisfying the exit criterion228 in one embodiment.

Similar features are also provided in the examples illustrated by FIGS.6C and 6D, wherein FIG. 6C depicts a structured collection procedure 70c which has a defined medical use case parameter 220 indicating that theprocedure is helpful for determining suitability of a bolus in regardsto a meal start. Likewise, FIG. 6D depicts a structured collectionprocedure 70 d which has a defined medical use case parameter 220indicating that the procedure is helpful for determining suitability ofan exercise equivalent to a carbohydrate intake. In addition to theabove examples, other such structured collection procedures may bedesigned to address other various medical use cases such as, forexample, the following: determining the effects of eating a particularfood on a biomarker level of a patient; determining the best time totake medication before and/or after a meal; and determining the affectof a particular drug on a biomarker level of a patient. Still otherstructured collection procedures can be provided which may be useful inaddressing questions concerning how best to initialize therapy for apatient, finding a determination of status of a patient diseaseprogression, finding the best ways to optimize a patient therapy, andthe like. For example, the clinician 14 can define and/or use apre-defined structured collection procedure 70 which looks at factorswhich may have an effect on the therapy of the patient. Such factors caninclude, for example, stress, menstrual cycle, pre-dawn effect,background insulin, exercise, bolus timing with respect to a meal, basalrate, insulin sensitivity, post-prandial behavior, and the like.

FIG. 6E shows a diagram structured collection procedure 70 comprisingone or more multiple sampling groups 262 each comprising a recurringschedule of events 222 provided between the entry criterion 226 and theexit criterion 228. In this example, the schedule of events 222comprises one or more events 237 occurring each day at consistent timesof day. As the structured collection procedure 70 in the process ofobtaining contextualized biomarker data from a diabetic patient 12 canspan over multiple days, even week and/or months before the exitcriterion 228 is met, one or more checks 264, such as for parameteradjustment, and/or evaluation of whether to re-run the sampling groups262, can also be provided between the entry and exit criterions 226, 228in one embodiment. The duration between such checks 264 can be used forphysiological system equilibration, evaluation of treatment efficacy, orconvenience. For example, either between each sample grouping 262 orafter a predefined number such sampling grouping 262 (as shown), ananalysis for the check 264 can be performed by the processor 102 todetermine whether an adjustment to any parameter in the collectionprocedure 70 is needed.

For example, such analysis may be either for a parameter optimization orefficacy assessment. For the parameter optimization, the processor 102can run calculations on the samples provided within a previous scheduleof events 222 or sample grouping 262, using information from prioroptimizations, clinician set parameters, and a collection or therapystrategy, recommends a new parameter value. For the efficacy assessment,the processor 102 can evaluate data not utilized by the optimizationanalysis. Additionally, it is to be appreciated that after a group ofsamples, i.e., sampling group 262, are taken the processor 102 can alsoevaluate the data from the sampling group 262, such as if such data isneed in order to alter/optimize a person's therapy. Adherence criteriacan be applied to perform this evaluation to the data of the samplinggroup 262 or set. For example, a first adherence criterion 224 can beused by the processor 102 to assess whether a minimum amount of data isprovided by the sampling group 262 and if not, for example, thealteration/optimization of the patient's therapy will not take place.Another adherence criterion 224 could permit the processor 102 assesswhether the data is acceptable to permit an adjustment called for by thecheck 264, such as looking at spread of the data, whether these is toomuch variability (noise), as well as other data attributes to use thedata. In this example, if meeting such adherence criterion, thenprocessor 102 has assessed that there is minimum risk that adjusting aparameter of the procedure could readily result in a severe event, e.g.,hyper- or hypoglycemic event. Lastly, an adherence criterion can be usedby the processor to assess the exit criteria based on the data ofsampling group, for example, the exit criterion is met when the datafrom the sampling group 262 satisfies the adherence criterion, such asfor example, discussed above, for the sampling group.

It is to be appreciated that collection or therapy strategies can becategorized into scale based (sliding or fixed) assessments or formulabased assessments. As input to the collection or therapy strategy, theprocessor 102 in one embodiment can utilize the data collected from apredetermined number of prior sample grouping(s) 262. This data can beeither used as individual points (only the formula based collection ortherapy strategies), or combined with filtering for use in a scale basedassessment. In another embodiment, for example, the result of a check264 performed by the processor 102 can also result in a status orrecommendation being provided by the processor 102 automatically. Suchstatus or recommendation may be e.g., a status of continuing withcurrent parameter values, a recommendation to change particularparameters, a recommendation to change the adherence and/or exitcriterion, a status that the processor 102 switched to a secondaryadherence and/or exit criterion based on the analysis performed on thedata from a prior schedule of events or prior sample grouping, or arecommendation to terminate the collection procedure, and the likes. Adiscussion of performing a structured collection using a structuredcollection procedure according to an embodiment of the present inventionis provided hereafter with reference made to FIG. 7A.

Structured Collection

FIG. 7A depicts a structured collection 300 for diagnostic or therapysupport of a patient with a chronic disease. The method 300 may beimplemented as instruction codes of a program running on a computer witha processor and memory, such as preferably clinician computers 25 (FIG.2) as stand-alone software, as part of software 34, or as softwareprovided as a service by server 52 via a secure web implementation overpublic network 50. Upon a processor 76 executing the program from memory78 of the clinician computer 25, as one function among others, theprocessor 76 after receiving a query for a medical use case and/orquestion, searches memory 78, computer readable medium 40, and/or server52 for all structured collection procedures 70 a-d, which matches thesubmitted query in step 302. For example, the processor 76 may read themedical use case parameter 220 of each available structured collectionprocedures 70 a-d and using a conventional search algorithm (e.g., list,tree, heuristics, etc.), provide on a display 82 a selection choice forthose structured collection procedure matching the query in step 304 inone embodiment.

In one embodiment, the list displayed can reflect, for example, thestructured collection procedures 70 a, 70 b, 70 c, and 70 d availablefor use from the server 52. In still another embodiment, the list ofselection choices displayed can be dynamically created based on a typeof medical use case the clinician 14 wishes to investigate. For example,prior to step 302, a list of selectable medical use cases can bedisplayed on the display 82 by the processor 76. In such an embodiment,the clinician 14, using the user interface device(s) 86 may selectedfrom among the displayed medical use cases, for example, the medical usecase “Determining meal effect on patient's therapy.” After the clinicianmakes such a selection, which the processor 76 receives as input fromthe user interface device(s) 86, the processor 76 after using decisionlogic (e.g., if . . . then) provided by the software 34 would thendisplay in step 304, for example, structured collection procedure 70 b(e.g., a structured collection procedure to determine a more accurateinsulin-to-carbohydrate ratio) and 70 c (e.g., a structured collectionprocedure to determine bolus timing in regards to meal start), and notstructured collection procedures 70 a and 70 d, which are structuredcollection procedures unrelated to the medical use case. Likewise, a“show all structured collection procedures” could also be a choice amongthe displayed medical use cases, in which the complete list of availablestructured collection procedures would then be displayed in step 304. Inanother embodiment, step 302 may be skipped and the processor 76 in step304 can just provide a display of the structured collection procedures70 a-d available in memory 78 of the clinician computer 25.

In step 306, a clinician using the user interface devices 86 can selecta structured collection procedure 70 on the computer 25 for diagnosticor therapy support. For example, the selecting process can includechoosing from the list displayed in step 304, which provided one or morestructured collection procedures. After the clinician makes such aselection in step 306, which the processor 76 receives as input from theuser interface device(s) 62, the processor 76 of the computer 25retrieves automatically from an electronic component, e.g., computermemory 78, server 52, or computer readable medium 40, and displays theselected structured collection procedure 70 on display 82 for viewing.

It is to be appreciated that each structured collection procedures 70 a,70 b, 70 c, and 70 d is based on a medical use case and has parametersdefining entry criteria 226, a schedule of events 222, adherencecriteria 224, and exit criteria 228. As mentioned above, the entrycriteria 226 establish the conditions needed to be met prior toobtaining biomarker data from the patient. Each event 237 of theschedule of events 222 comprises a performance time, patient guidance toperform the event, patient actions, a request for information from thepatient, a request for collection of at least one type of biomarker datafrom the patient, and combinations thereof. The adherence criteria 224is used to qualitatively assess whether an event 237 performed accordingto the schedule of events 222 provided data which is acceptable toaddressing the medical use case upon which the structured collectionprocedure 70 is based. Additionally, as mentioned above, the exitcriteria 228 establish the conditions needed to be met prior to exitingthe structured collection procedure 70.

In step 310, after the processor 76 displays the selected structuredcollection procedure 70, the clinician 14, to meet the needs of thepatient 12 and/or interests of the clinician, may adjust any one of theparameters 222, 224, 226, and 228 which are also displayed on thedisplay 82. Safe guards may be implemented to ensure that only theclinician 14 can modify such parameters and/or run the software 34, suchas via password protection. The processor 76 receives any such changesto the parameters 222, 224, 226, and 228 as input via user interfacedevices 86 and saves the revised structured collection procedure 70 inmemory 78. Next, in step 312, the selected structured collectionprocedure 70 is prescribed on the computer 25 to the patient 12 by theclinician 14, wherein the processor 76 of the computer 25 provides asoutput the selected structured collection procedure 70 to the patient 12to perform. For example, in step 314, the prescribed structuredcollection procedure 70 is implemented electronically on a processorbased device, such as collection device 24, or any of the other abovedescribed devices 18, 28, and 36 (FIG. 1), as part of the software 34 orin other embodiment, portions thereof as part of paper tool 38.

In one embodiment, the prescribed structured collection procedure 70 maybe implemented from the clinician computer 25 (FIG. 2) to the collectiondevice 24 via communication link 72, via the public network 50 through awebpage and/or made available for downloading on server 52. In stillother embodiments, the prescribed structured collection procedure 70 canbe provided through the computer readable medium 40 and loaded by one ofthe devices 18, 24, 28, and 36, downloaded from another one of thedevices 18, 24, 25, 26, 28, and 36, or downloaded via cell phone ortelephone connection from server 52. Notice that anew/updated/prescribed structured collection procedure 70 available foruse on the devices 18, 24, 25, 26, 28 and 36 may be provided in anystandard fashion, such for via postal letters/cards, email, textmessaging, tweets, and the likes.

Customizing a Structured Collection Procedure

FIG. 7B conceptually illustrates one example of a pre-defined structuredcollection procedure 70, which has a defined medical use case parameter220 indicating that the procedure is helpful for medical use cases orquestions which need to know the trends in blood glucose (bG) levels ofa patient and/or the relationships between blood glucose values and timeof day, meal size, and energy level. As mentioned above previously, theuse case parameter 220 can be used as an identity tag in which theprocessor 102 may locate the associated structured collection procedure70 in response to a search query, such as, for entered use case orquestion. For example, the search query can be entered into thecollection device 24 via the user interface 146 and/or received from theclinician computer 25. Such a search query may result from a desire toknow which uses case can be addressed by the structured collectionprocedures 70 currently available on the collection device 24, or toknow which structured collection procedure 70 would be useful to addressa particular use case or question. Therefore, the use case parameter 220in one embodiment permits a structured collection procedure 70 to beautomatically chosen by the processor 102 from a plurality of structuredcollection procedures 70 a-d, such as provided in memory 110, memory 78,computer readable medium 40, and/or server 52 based on a selection, suchas from a displayed list on the display 108 provided by the processor102, or from input received by the processor 102 from the user interfaceof a defined medical question. In other embodiments, the use caseparameter 220 may also indicate the structured collection procedure 70is also useful for showing relationships between bG level values andtime of day, meal size, and/or energy level.

In one embodiment, the pre-defined parameters of the structuredcollection procedure 70 can be displayed for modification/customizationby the processor 102 of the collection device 24 on the display 108and/or by the processor 76 of the clinician computer 25 on the display82 by an authorized user. Such an authorized user may be identified, forexample, on the collection device 24 and/or the clinician computer 25 bya password entered via the user interface 146, 86, respectively. In suchan embodiment, the pre-define parameters of structured collectionprocedure 70 can be displayed on the display 108, 82 in whichcustomizable parameters can provide editable or selectable variables viadrop-down boxes with various selection choices, radio buttons, checkboxes, formatted fields requesting a specific type of information(mm-dd-yyyy, number, letter, etc.), text boxes to enter messages to bedisplayed, and the likes. The structured collection procedure 70 can bedisplayed for editing in tabular format (as illustrated) in oneembodiment or in a sequential manner listing one parameter at a time ina scroll-through fashion in another embodiment. In still anotherembodiment, structured collection procedures can be provided whichcannot be modified.

As shown by FIG. 7B, the structured collection procedure 70 may furthercomprise parameters defining one or more criterions setting theconditions needing to be met by the patient 12 to start of thestructured collection procedure, i.e., entry criterion(s) 226, to endthe structured collection procedure i.e., exit criterion(s) 228, andcombinations thereof. In one embodiment, the processor 102 of thecollection device 24 uses the one or more criterions to automaticallystart, evaluate, and end the structured collection procedure 70 if thecondition(s) defined by the structured collection procedure are met. Instill another embodiment, adherence criterion(s) 224, which are theconditions needing to be met in order for the collected datum/data to beaccepted, can also be provided in the structured collection procedure70.

As also shown in FIG. 7B, the structured collection procedure 70 furthercomprise parameters defining one or more (collection) events 237 whichtogether form the schedule of events 222. Each of the events 237comprises one or more requests 240, e.g., for a measurement from themeasurement engine 138 of a biomarker value for a sample provided to thebiosensor 140, and/or for information to be entered by the patient viathe user interface 146 such as in response to a question presented bythe processor 102 on the display 108. In the illustrated embodiment, therequests 240 are for a bG measurement, a meal size indication (S, M, orL), and an energy level indication (1, 2, 3, 4, 5), in which 1 is lowestand 5 is highest. Other such requests 240 can include indicating whetherthe patient exercised, indicating a particular food that was consumed,indicating which medicine was administered, indicating dosage of themedicine administered, and the like may also be provided in otherstructured collection procedures 70. In the illustrated embodiment, thecollection events can be customized by selecting which request 240 theprocessor 102 should perform via a yes/no selection box.

The structured collection procedure 70 may also include guidance 230 andtiming or performance time 238 associated with each of the collectionevents 237 as well as with each of the entry, exit, and adherencecriterion/criteria 226, 228, and 224. Such guidance 230 is provided bythe processor 102 to the display 108 upon the occurrence of theassociated collection event 237 or other parameters. For example, acollection event 237 for a bG measurement before breakfast may also havea request 240 for an indication of the energy level of the patient.Therefore, in this example, the associated guidance 230 which states,“Please indicate energy level” is provided on the display 108 by theprocessor 102. It is to be appreciated that the guidance 230 is a textbox, field, area, which enables for information to be provided to thepatient to help the patient in performance of the structured collectionprocedure 70. In this example, selection of a number from 1 to 5 may bemade via press of one of the buttons 147, 149 or via the touch screeninterface if provided by display 108 as a data entry for such a request237, which is then stored by the processor 102 in memory 110 of thecollection device 24 as part of a data file 145 (FIG. 4) for thestructured collection procedure 70.

The timing parameter 238 of the structured collection procedure 70 isused to specify for any one of the associated collection event 237, theentry, exit, and adherence criterion/criteria 226, 228, 224, either aspecific date and/or time (mm-dd-yyyy, hh:mm), or a period (n) after apreceding collection event in which to perform the associated collectionevent. The periods n₁, n₂, n₃ in the illustrated embodiment for therespective collection events 237 indicate hours, but in otherembodiments can be indicated in minutes or seconds. In anotherembodiment, the timing or performance time parameter 238 for anassociated collection event 237 and for the entry, exit, and adherencecriterion/criteria 226, 228, 224 can be modified by another collectionevent and/or by the criterion/criteria.

For example, in the illustrative embodiment, the entry criterion 226 ismodified by the adherence criterion 224 by adding a day if the guidance230 provided in the form of a question “Are you willing to conduct atest over 3 consecutive days?” is not affirmed by the patient 12 e.g.,via a “No” selection provided on the collection device 24. In thisillustrated example, the “Affirms guidance” may be a drop down selectionprovided in a combo box for customizing the adherence criterion 224 ofthe associated collection event 237, which when selected causes theprocessor 102 to wait for the accepted/not accepted input (e.g., viabuttons 147, 149) before executing the remaining logic (“if not add 1day to timing”) of the adherence criterion 224. Still further in thisexample, the processor 102 in accordance with the logic provided in theadherence criterion 224 associated with the exit criterion 228, can setthe timing or performance time parameter 238 of the exit criterion 228to the date (mm-dd-yyyy) that is 3 days after completing the entrycriterion 226. It is to be appreciated that the various possiblecombinations of logic statements which may be performed by thestructured collection procedure 70 can be pre-defined and selected by adrop down box in order to be customized in one embodiment, and/or logicstatements can be built in another embodiment.

The structured collection procedure 70 can also includes an optionsparameter 232 associated with each of the collection events 237 as wellas with each of the entry, exit, and adherence criterion/criteria 226,228, 224. The options parameter 232 can have a customizable value(s) togovern whether the data and/or results of the associated collectionevent 237 or any of the other parameters e.g., entry, exit, andadherence criterion/criteria 226, 228, 224, in the structured collectionprocedure 70 meets a particular condition such that still furtherprocessing may be carried out by the processor 102 if such acondition(s) is meet. For example, such options can be to have theprocessor 102 automatically send a message to the physician indicatingthat the patient has started the structured collection procedure 70 viasatisfying the entry criterion 226, or to provide a message to thepatient and/or the physician if the patient fails a collection event 237by not satisfying an adherence criterion, or to provide a message to thephysician when the patient completes the structured collection procedure70 when the exit criterion is satisfied, or combinations thereof. Forexample, such an options parameter 232 can have a global list of suchactions which is selected on the display 108, for example, by a selectedvalue from a range of values associated with each option. For example,the options for each parameter can be customized via selecting from adrop down box having option choices (e.g., 1, 2, 3, 4, 5, . . . , A, B,C, etc.) and in which, for example, Option 1 of having the processor 102provide a message to the physician if the patient fails a collectionevent 237 (e.g., by not satisfying an adherence criterion), is shownselected for the before breakfast collection event 237. An example inthe context of patient 12 being diabetic is provided hereafter toillustrate further such features provided on a collection device 24according to the present invention.

A typical patient with Type 2 diabetes may measure his/her blood glucoseonce per day after waking up in the morning. At a routine office visit,the patient's HbA1C result is found to be elevated. The physicianrecommends that the person goes through three days of intensifiedglucose monitoring, and selects the structured collection procedurewhich is useful for this purpose. The structured collection procedure 70is then customized as discussed above such that during these three dayscollection events 237 are defined with a number bG measurement requests240 such that the patient can be requested by the processor 102 tomeasure his/her blood glucose before and two hours (e.g., n₁=2) afterbreakfast, before and two hours (n₂=2) after lunch, before and two hours(n₃=2) after supper, and at bedtime. Additionally, the patient 12 can berequested via other associated requests 240 for each collection event237 to provide an assessment of the relative size of the ingested mealsat the appropriate times as well as an indication how he/she feels withregard to energy level. In the illustrate embodiment of FIG. 7B, theprocessor 102 can request the indication of energy level with eachcollection event 237 and the assessment of the relative size of theingested meals every other collection event 237 (i.e., after the meal).Furthermore, the physician has provided a condition via adherencecriterion 224 of having to perform the meal assessment within ±30minutes of period (n) of the associated collection event 237 in orderfor such information to be useful in the assessment. Such information isuseful to contextualize the collected data and for the analysisperformed on the collected data.

Additionally, the physician would like to be notified when the patienthas failed to complete the “before breakfast” collection event 237.Therefore, to facilitate the notification option, the physiciancustomizes the structured collection procedure 70 by set the optionsparameter 232 associated with the “before breakfast” collection event,via a drop down box to “Send a message to the physician if adherencecriterion fails.” All other collection events 237 have their associatedoptions parameter 232 default to indicate that the processor 102 is notto take any additional action with regards to the options parameter. Itis to be appreciated that the above described features and arrangementsin the illustrated embodiment of FIG. 7B, provide a simple andconvenient interface and method for customizing a structured collectionprocedure, such as for parameter adjustments carried out in step 310 ofmethod 300 previously discussed above in reference to FIG. 7A.

Implementing and Performing a Structured Collection Procedure

FIG. 8A shows a flowchart of the method for implementing and performinga structured collection procedure 70 to obtain contextualized biomarkerdata from a patient 12 according to an embodiment of the invention. Itis to be appreciated that a number of structured collection procedures70 a-d (FIG. 2) prescribed in step 312 and implement in step 314 (FIG.7A) may be stored in memory 110 (FIG. 3) of the device 24 and selectedfor execution at any desired time. For example, upon pressing a certaincombination of the buttons 147, 149, the patient can select a desiredstructured collection procedures 70 a-c and the date when to start astructured testing collection i.e., a set mode function. For example, adate range to choose from may be to begin the testing tomorrow and endat today +90 days, which the processor 102 can also recorded in the datafile 145 (FIG. 4) as part of the setup data 163. In such animplementation, the processor 102 as instructed by the software 34 readsthe setup data 163 for the selected structured collection procedure 70and indicates on the display 108 that the device 24 is in a structuredtesting mode, for example, from one day before the chosen focusedtesting start date until the end of the structured collection procedure.

It should be appreciated that multiple structured collection procedures70 a-d can be executed sequentially or simultaneously at any given time.However, in one embodiment, the software 34 permits the user only toschedule another structured collection procedure 70 if the start date islater than the end date of the current structure collection procedure 70being executed. The software 34 also permits the user to override ascheduled date for a structured collection procedure 70. If a structuredcollection procedure 70 is scheduled and the user enters the set modefunction again, the software 34 causes the processor 102 to display thescheduled date on the display 108 as the default date; if the user exitsthe set mode without modifying the date, the previously scheduled datestays active. If a structured collection procedure 70 has started, thesoftware 34 permits the user to enter the set mode and cause theprocessor 102 to cancel the current structured collection procedure 70.Upon cancellation, in one embodiment, the software 34 causes theprocessor 102 to de-tag (e.g., null the unique identifiers 167) the datarecords 152 in the data file 145 for the data collected for thecancelled structured collection procedure 70.

Upon reaching the procedure start in step 316 (FIG. 8 a), the processor102 evaluates the whether entry criterion(s) 226 is met in step 318 tobegin the structured collection procedure 70 selected to obtainbiomarker data to address a predefined use case or question (e.g., usecase parameter 220). In step 320, the processor 102 specifies requests240 according to their associated timing 238 for each event 237 in theschedule of events 222 for the structured collection procedure 70. It isto be appreciated that the schedule of events 222 provides a samplingplan for biomarker data collection that is performed by the processor102 to obtain biomarker data in a predefined context. In performing theschedule of events 222 in step 320, the software 34 causes the processor102 to assign a unique identifier (e.g. incremental count) 167 in a daterecord 152 which corresponds to each event 237 in the structuredcollection procedure 70. Optionally, each criterion 226, 228, 224 mayalso be provide with a date time stamp 169 to indicate when suchcriterion was satisfied, if desired.

Adherence criterion 224 is then applied to the input received (e.g.,biomarker data or information) in response to a request 240 to determinewhether the input received meets the adherence criterion 224. When astructure collection procedure 70 has started, all data collectedaccording to requests 240 in the structured collection procedure 70 andwhich satisfy the adherence criterion 224, if required in step 322, arethen assigned (tagged) in the data file 145 by the processor 102 withthe unique identifier 167 in step 324. It is to be appreciated that theunique identified also serves to associates the collected data e.g.,data values 256 with their event 237, the request 240, and a date-timestamp 169 to indicate when the collection in response to the request 240was received by the processor 102. While a structured collectionprocedure 70 is being executed, in one embodiment the software 34permits the user to perform a measurement on the device 24 at any timewithout interfering with the episode.

In one embodiment, the software 34 permits reminders for biomarkermeasurements to be ‘snoozed’ as mentioned above for a period, such asfor example, 15 minutes and up to a number of times, for non-criticalmeasurements. In another embodiment, biomarker measurements or dataentries that are performed close enough in time to a request 240 in step320 are designed as valid measurements or data entry for the request 240by the software 34. As such, the processor 102 will tag the associateddata record 152 for the event 237 with the unique identifier 167 forsuch a biomarker measurement or data entry accordingly. In the case ofbiomarker measurements, if the measurement is accepted as valid for therequest 240, the software 34 causes the processor 102 to prompt the userto input additional information if needed by the structured collectionprocedure 70 to provide context 252 for data resulting from the request240. Such additional input, may include, for example, a rating of energylevel from 1 to 5, where 1 is low and 5 is high; meal size from 1 to 5where 1 is small and 5 is large, and exercises from yes or 1 to meanover 30 minutes, and no or 2 to mean less than 30 minutes. Suchadditional information or contextual information 156 when inputted viathe user interface 146 is stored by the processor 102 in the data file145 associated with the unique identifier 167 for the data event request240 requiring the additional information also in step 324.

In one embodiment, biomarker measurements determined by the processor102 as not being close enough in time to the data event request 240defined by the structured collection procedure 70 will not be taggedwith a unique identifier 167 in the data file 145 by the processor 102.Such is illustrated in the shown data file 145 with request 240 d anddata values 256 d not being associated with a unique identifier 167e.g., <null>. An example of a definition of ‘close enough in time to thecollection procedure’ as instructed by the structured collectionprocedure 70 and/or software 34 to cause the processor 102 to make sucha determination may be defined as being relative to a prescheduled timeor a snoozed time. For example, for pre-prandial measurements up to 15minutes in anticipation is acceptable; for post-prandial measurements,up to 10 minutes in anticipation is acceptable; and for bedtimemeasurements, up to 15 minutes in anticipation is acceptable. Otherdefinitions may be provided in other structured collection procedures 70and/or software 34.

In step 326, the processor 102 then evaluates whether the exit criterion228 for the selected structured collection procedure 70 is satisfied. Ifnot, then the processor 102 continues with performance the schedule ofevents 222 until the exit criterion 228 is satisfied. Upon satisfyingthe exit criterion 228, the collection procedure 70 ends in step 328. Inone embodiment, the structured collection procedure 70 may also end ifin step 318, the entry criterion 226 is also not met.

In some embodiments, the structured collection procedure 70 can beconfigured for performance as a paper tool 38; diabetes software 34integrated into a collection device 24 such as a blood glucose meter 26;diabetes software 34 integrated into the computing device 36, such as apersonal digital assistant, handheld computer, or mobile phone; diabetessoftware 34 integrated into a device reader 22 coupled to a computer;diabetes software 34 operating on a computer 18, 25 such as a personalcomputer; and diabetes software 34 accessed remotely through theinternet, such as from a server 52. When diabetes software 34 isintegrated into a collection device 24 or a computing device 36, thediabetes software 34 can prompt the patient to record diary informationsuch as meal characteristics, exercise, and energy levels. The diabetessoftware 34 can also prompt the patient to obtain biomarker values sucha blood glucose values.

GUI Interface Providing a Selectable Structured Collection Procedure

FIG. 8B shows a method of implementing the structured collectionprocedure via a graphical user interface provided on a collection device24, which when executed on the collection device, cause the processor102 to perform the following steps. Upon pressing a certain combinationof the buttons 147, 149, the patient 12 can scroll to the structuredcollection procedure 70 available for selection in a list 329 providedby the processor 102 on the display 108 of the collection device 24 instep 330. If desiring to start the structured collection procedure, thepatient 12, for example, selects via pressing an OK button 151 in step332, the desired structured collection procedure 70. In this example,the entry criteria 226 (FIG. 6) of the structured collection procedure70 provides information in step 334 which the processor 102 displays tothe user on the display 108. After reading the displayed information,the user presses any button in step 336 in which the next procedure inthe entry criteria 226 is performed by the processor 102. In thisillustrated example, as part of the entry criteria 226, a question isthen asked in step 338 by the processor 102. If the patient 12 is stilldesirous of starting the structured collection procedure, the patient 12selects the OK button 151 in step 340; otherwise, any other press viabutton 147, 149 will cause the processor to go back to the list 329,thereby stopping the set-up procedure for the structured collectionprocedure 70.

After the patient 12 presses the OK button 151, the processor 102 instep 342 will provide on the display 108 an alarm clock 343 for settingthe time to begin the selected structured collection procedure 70. It isto be appreciated that all the required events 237 for biomarkersampling, patient information, etc., is automatically schedule by theprocessor 102 in accordance with the schedule of events 222 for thestructured collection procedure 70 in which timing, values, questions,etc., therein may have been adjusted by the clinician 14 as discussedpreviously above in reference to FIGS. 7A and 7B. Therefore, other thanentering the start time as permitted by the entry criteria 226, no otherparameter adjustments in the structured collection procedure 70 isrequired by the patient 12 (or permitted in one embodiment).

In the illustrated embodiment, the patient in step 344 can adjust thestart time of the structured collection procedure for the next day,e.g., Day 1, via buttons 147, 149. Upon confirming the start time instep 346 via pressing the OK button 151, the start time is recorded inmemory 110 as part of the setup data 163 in the data file 145 (FIG. 4)for the structured collection procedure 70 by the processor 102. Theprocessor 102 then displays the selection list 329 on the display 108 instep 348, thereby completing the set-up procedure, which satisfies theentry criterion 226, and indicates on the display 108 that thecollection device 24 is in a structured testing mode 349.

It should be appreciated that in on embodiment multiple structuredcollection procedures can be executed sequentially or simultaneously atany given time, and hence in one embodiment the mode 349 provided on thedisplay 108 will indicated which structured testing is being performed.However, in one embodiment, the software 34 does not permits the user toschedule another structured collection procedure, unless the start dateis later than the end date of the current structured collectionprocedure being executed via the user interface 146. It is to beappreciated that processor 102 may re-schedule the following structuredcollection procedures automatically if the current structured procedureis still running due to the exit criteria 228 not being met. Thesoftware 34 in another embodiment may also permit the user to override ascheduled date for a structured collection procedure. If a structuredcollection procedure is scheduled and the user enters the set modefunction again, the software 34 causes the processor 102 to display thescheduled date on the display 108 as the default date; if the user exitsthe set mode without modifying the date, the previously scheduled datestays active. If a structured collection procedure has started, thesoftware 34 permits the user to enter the set mode and cause theprocessor 102 to cancel the current structured collection procedure, ifdesired.

In step 350, an alarm condition 351 can be provided by the processor 102the next day (as indicated by the symbol Day1) as was set in theabove-mentioned procedure the previous day (as indicted by the symbolStart Up). Upon the user selecting any button 147, 149, 151 in step 352,the processor 102 as instructed by schedule of events 222, provides afirst scheduled event 237 which is information 353 to be displayed ondisplay 108 in step 354, which the patient 12 acknowledges with anybutton 147, 149, 151 being pressed in step 356. Next in step 358, theprocessor 102 is instructed by the schedule of events 222 to execute asecond scheduled event, which is to display on the display 108 aquestion 359 for the patient, which the patient 12 acknowledges with anybutton 147, 149, 151 pressed in step 360. In the illustrated embodiment,the patient in step 362 indicates the start time of breakfast in minutesfrom the wake up alarm 351 previously acknowledged in step 352. Uponconfirming the meal start time in step 364 to the processor 102, viapressing the OK button 151, the meal start time is recorded in memory110. For example, the meal start time is recorded in the data file 145in the associated data record 152 as data for the event 237 by theprocessor 102. Additionally, in step 366, the processor 102 displays tothe patient 12 the information regarding the timing for the nextschedule event as a reminder. In step 368, upon reaching the nextscheduled event indicted by the schedule of events 222, the processor102 provides a request 240 on the display 108 for the patient to take ameasurement, e.g., a blood glucose measurement. Additionally, in step370, the processor 102 also makes a request 240 for information on thesize of the meal that is to be ingested as required by the schedule ofevents 222 in order to provide contextual information 156 to themeasurement value.

As mentioned above previously, for each event the software 34 causes theprocessor 102 to assign a unique identifier (e.g. incremental count) 167(FIG. 4) to the data of each request 240 provided in the schedule ofevents 222 which meet the adherence criterion 224 in the associated daterecord 152 for the event 237. Therefore, while the structured collectionprocedure is being executed, the software 34 permits the user to performa measurement on the collection device 24 at any time out side theschedule of events 222. Such a measurement since not being performedaccording to a request 240 will not be evaluated for the adherencecriterion 224, and thus will not be provided with a unique identifier167 in the date file but will only be provided with a date-time stampand its measurement value. Such data is still recorded in the data file145, as such data may still be useful for another analysis.

In another embodiment, the software 34 also permits reminders forbiomarker measurements, such as provided in step 238. For example, inone embodiment, the processor 102 provides an alarm and/or alert messagefor a reminder via the indicator 148 and/or on the display 108,respectively, to provide a measurement. For example, at the time 238 ofa particular request 240 for taking a biomarker measurement (orreading), the processor 102 prompts the patient 12 by al leastdisplaying on the display the message, “It is now time for yourreading.” An audible alarm and/or tactile alarm (vibrations) can beprovided by the processor 102 via indicator 148 in another embodiment.For example, in one embodiment, the collection device 24 will providesuch a prompt even when already powered on, such as by the patient 12for another reason, e.g., to conduct a non-scheduled event, when in, forexample, a window of time in which to take the requestedmeasurement/reading, or even when powered downed, such as in a standbymode, by waking up to provide the reminder via the prompt. In anotherembodiment, the provided reminder or prompt can be ‘snoozed’ for apre-defined period as mentioned above, that still falls within thewindow of time in which to take the requested (critical)measurement/reading such as for example, 15 minutes or any other suchsuitable time that falls in the window of time. It is to be appreciatedthat the snooze feature for a measurement/reading that is consideredcritical to the structured testing procedure 70, e.g., ameasurement/reading needed for helping to address the medical use caseor question, needed to meet adherence criteria 224, and/or needed insubsequent analysis for some determination, etc., the snooze featurewill not extend the request 240 beyond the window of time provided bythe collection procedure 70 via, e.g., adherence criterion 224 for therequest 240. For example, in one embodiment one or more events 237 inthe schedule of events 222 can be pre-defined as critical as well asbeing a primary sample via use of the options parameter 232 (FIG. 7B)provided in the structured collection procedure 70. For example, anevent 237 which is designated as critical is one that cannot be missed,but if missed can be replaced by another sample already in the date file145. In still another embodiment, the snoozing can be up to a number oftimes, for non-critical measurements. For example, certain events 237 inthe structured collection procedure 70 could be designated as having anon-critical request 240, which can be snoozed, such as via selectingsuch an option that is provided as one of the options parameter 232(FIG. 7B). The options parameter 232 in this embodiment could forexample provide the snooze option as well as a selectable time interval(e.g., 1-60 minutes, etc.) and a selectable number of times (e.g., 1-5,etc.) that the user is permitted to snooze the request 240. In stillanother embodiment, the collection device 24 permits for an alarm shutoff i.e., the indicator 148 if providing the reminder (audible,vibratory) can be shut off for the entire window of time via the userinterface 146, but wherein processor 102 still accepts themeasurement/reading as long as it is made in the window of time. Instill another embodiment, the collection device 24 provides a skipreading option also received by the processor 102 via a selectionentered using the user interface 146, e.g., from a list of selectableoptions, such as for example, snooze, alarm shut off, skip reading,provided on the display 108, in which again no reminder/prompt will beprovided as patient 12 has indicated to the processor 102 that he/shedoes not want to take that particular requested measurement/reading. Itis to be appreciated that selecting the skip reading selection optioncan result in an adherence event 242 resulting in further processing,such as discussed previously above in early sections, if adherencecriterion 224 had been associated with the event 237 prompting therequest 240.

In still another embodiment, the adherence criteria 224 can requirebiomarker measurements to be performed close enough in time to a dataevent request 240. Therefore, if such biomarker measurements areperformed within the period specified by the adherence criteria 224, theprocessor 102 can indicate that the measurements or data entry for theevent is acceptable and tags (i.e., assigns the unique identifier 167)the value of the biomarker measurement or data entry in the data file145 accordingly. In the case of biomarker measurements, if themeasurement is accepted as valid for the data event request 240 (i.e.,meets the adherence criterion(s) 224), the schedule of events 222 maycauses the processor 102 to prompt the user to input additionalinformation if needed by the structured collection procedure 70, such asmentioned above regarding step 370 to provide contextual information 156(i.e., context) to the measurement received in response to a request240.

Such contextual information 156 when inputted via the user interface 146can be stored by the processor 102 in the data file 145 associated withthe unique identifier 167 for the data event request 240 requiring theadditional information. Biomarker measurements determined by theprocessor 102 as not being close enough in time to the data eventrequest 240 as defined by the adherence criteria 224 will not be taggedin the data file 145 by the processor 102. Such is illustrated in theshown data file 145 (FIG. 4) with data event request 240 d and datavalues 256 d not being associated with a unique identifier 167. Anexample of a definition of ‘close enough in time to the collectionprocedure’ as instructed by the adherence criteria 224 to cause theprocessor 102 to make such a determination may be defined as beingrelative to a prescheduled time or a snoozed time. For example, forpre-prandial measurements up to 15 minutes in anticipation isacceptable; for post-prandial measurements, up to 10 minutes inanticipation is acceptable; and for bedtime measurements, up to 15minutes in anticipation is acceptable. Other definitions may be providedin other adherence criteria 224 for other events in the schedule ofevents 222 as well as in other structured collection procedure.

In the illustrated embodiment, the user uses the buttons 147, 149 toscroll to a selection, which is entered by the processor in the datarecord 152 for the associated request 240 via pressing Okay button 151in step 372. In one embodiment, the meal size can be indicated via anumber range, such as for example, from 1 to 5, where 1 is small and 5is large. In the illustrated embodiment, additional input for contextualinformation 156 regarding a rating of energy level from 1 to 5, where 1is low and 5 is high is requested in step 374, which is entered in thedata file 145 as mentioned previously above via the processor 102receiving input for the request 240 by using the user interface 146 instep 376. In other embodiment, other contextual information 156 mayinclude indicating whether the patient exercised and/or how long. Forexample, the user interface 146 may be use in which yes or 1 to meanover 30 minutes, and no or 2 to mean less than 30 minutes. In theillustrated embodiment, as the exit criterion 228 is now meet viasuccessfully performing steps 368-376, the structured collectionprocedure 70 ends in step 378, wherein the processor 102 again displaysthe list 329, such that the patient 12 may perform other tasks on thecollection device 24 if so desired. Reference is now made to FIG. 9hereafter.

Method of Contextualizing Biomarker Data

FIG. 9 depicts a method 388 of contextualizing biomarker data fordiabetes diagnostics and therapy support according to an embodiment ofthe invention. It is to be appreciated that in the previous embodimentsdiscussed above with reference to FIGS. 8A and 8B, the contextualinformation 156 was requested and recorded with the associated biomarkervalue by the processor automatically during the structured collectionprocedure 70. However, in embodiments where such automation is notprovided on the collection device 24, and the patient is using a papertool 38, the collection data can be later associated with its contextualinformation 156 after, for example, the structured collection procedure70 is performed in step 390 to create at least data event values 256. Ifnot already done by the collection device 24, such as in the case of adevice with limited memory and processing power or when recordings aremade on paper tool 38, such data may be provided to another one of thedevices 18, 25, 36 that is running the software 34 and has the abilityto associate at least the data event values 256 (FIG. 4) with theirrespective data event requests 240. This associating of at least thedata event values 256 with their respective data event request 240, thedate-time stamp 169, and the contextual information 156 results incontextualized (self-monitoring) data 170 in step 392.

It is to be appreciated that data as used in structured testingaccording to the present invention deals with the prospective collectionof contextualized data. Considering FIG. 10A, in this example, theinherent advantage of context becomes clear when one considers theutility of having a subway map on the left-hand side without context andone on the right-hand side with context, which makes it possible toeasily navigate the system and travel from one place to another. In asimilar manner, contextualization can play an important role indiabetes. Context associated with data, for example, can be due totherapy, an event (such as meals, exercise, an event 237, request 240,etc.), and time of request for data collection itself (e.g., timing238). As such, any data collected by the patient with measured valuescan be contextualized by being associated with one or more the abovementioned factors e.g., therapy, events, and time, each of which arediscussed further hereafter.

Therapy can be defined, for example, as an on-going treatment intendedto alleviate the patient's impaired glucose control. This treatmentgenerally involves anti-diabetic agents such as insulin, oralmedications, and diet and exercise. A therapeutic (or a therapeuticcombination) has a specific pharmacodynamic effect on a patient'sglycemia owing to different mechanisms of action. A change in either thedose(s) of the therapeutic(s) or a change in the therapeutic(s) itself,will lead to a change in the patient's glucose control. Consequently,the collected bG data is strongly linked to underlying therapy and doseand this information is used to contextualize the data. A change in doseor therapeutic will lead to a different context. It is to be appreciatedthat the therapy context can be set by the clinician 14 in consultationwith the patient at the time of designing the collection procedure 70,such as discussed previously above with regards to FIG. 5A.

In one embodiment, the events 237 in a collection procedure 70 caninclude specific conditions around bG measuring points that play a rolein altering the patient's normal glucose levels. As mentioned previouslyabove, events 237 can be meal or exercise based, and are pertinent fordata contextualization. In this context, the underlying assumption isthat the patient operates, more or less, under a well-defined schedule.At the time of creating the collection procedure 70, the patient 12 candiscuss lifestyle events with the clinician 14 so that the collectionprocedure 70 can be tailored according to the needs of the patient 12.As an example and with reference to FIG. 10B, consider a typicalcollection procedure 70 whereby the patient 12 does not exerciseregularly so that the majority of the events are meal based events thatconsist of breakfast, lunch, and dinner. Such a lifestyle of the patient12 leads to six candidate points for bG measurement (pre and post foreach of the meals) for the schedule of events 222 in the collectionprocedure 70. During the process of creating/customizing (FIG. 5A and/orFIG. 7B) of the collection procedure 70, the clinician 14 may specifythat the patient collect one or more or all of these points as per theschedule of events 222 of the collection procedure 70. Any datacollected in addition to these points, i.e., outside the requirements ofthe collection procedure 70, can be classified as non-collectionprocedure readings by the processor 102. In a similar manner, for a Type1 diabetic patient that exercises regularly, the clinician 14 cantailor/customize a collection procedure 70 to include additionalmeasurements around the exercise event. The event information, in thisexample, is then used to contextualize the data in an appropriate mannerdepending on the event 237.

Time represents the actual time at which a measurement is made and is inabsolute terms, e.g., date-time stamp 169 (FIG. 4). Additionally, timecan also be represented in terms of deviations, i.e., offset from aparticular event. As an example, a postprandial reading is taken at aspecific time after a meal and this time may be different acrossdifferent days. This scenario arises as the patient may not be able totake an event based reading t the same time every day. Consequently,there is a distribution of times at which the same measurement has beenmade at different days. The knowledge of this distribution can becomeuseful for analysis of such timing as well as the parameter timing 238in the collection procedure 70.

Additionally, with the contextualized data 170, the physiological stateof the patient 12 at the time of the measurement can be described. Thepatient's physiological state can influence a biomarker value, soknowledge of the patient's physiological state aids in the understandingof a biomarker value. The biomarker data can be contextualized becausethe biomarker data is collected in the context of predetermined eventssuch as last time of meal, meal type, meal distribution, exerciseinformation, sleep quality, sleep duration, waking time, and stressorssuch as illness and the like. Time-resolved data permits interpretingthe biomarker data in context with other information, such as compliancewith a structured collection procedure 70 and patient lifestyle events.

Referring again to FIG. 9, the contextualized data 170 is evaluatedusing adherence criterion (or criteria) 224 to generate acceptedcontextualized data 395 that meets the adherence criterion 224 in step394. As the adherence criterion 224 can provide a basis for comparisonof a data event value 256 with a standard, so the data event value canbe either accepted and used or rejected and not used, the adherencecriteria 224 can be used to filter data in one embodiment. In anotherembodiment, step 394 may precede step 392.

FIG. 11, for example, shows a diagram of accepted contextualized data395 intermingled with non-acceptable contextualized data 397. Thediagram vertical axis shows biomarker values 256 including context 252in the form of a biomarker setpoint, a biomarker upper limit, and abiomarker lower limit. The diagram horizontal axis shows performancetimes 238 of measurement requests 240 and a sleep period event 237 inwhich the actual sleep surpassed a recommended minimum amount of sleepas indicated by the dashed line. The accepted contextualized data 395 isthat which met the adherence criterion 224. The non-acceptablecontextualized biomarker data 397 are either not within the structuredcollection procedure 70 or did not meet adherence criterion 224. Byexcluding the non-acceptable contextualized biomarker data 397, theaccepted contextualized biomarker data 395 can help improvedecision-making Statistical techniques can be used to view the acceptedcontextualized biomarker data 395 in a form that conveys additionalinformation to a clinician 14. Examples of statistical techniquesinclude regression methods, variance analysis, and the like. Hereafterfurther details about another embodiment of the software 34 areprovided.

Software

As mentioned above in previous sections, the software 34 can operate onthe patient computer 18, the collection device 24, a handheld computingdevice 36, such as a laptop computer, a personal digital assistant,and/or a mobile phone; and paper tools 38. The software 34 can bepre-loaded or provided either via a computer readable medium 40 or overthe public network 50 and loaded for operation on the patient computer18, the collection device 24, the clinician computer/office workstation25, and the handheld computing device 36, if desired. In still otherembodiments, the software 34 can also be integrated into the devicereader 22 that is coupled to the computer (e.g., computers 18 or 25) foroperation thereon, or accessed remotely through the public network 50,such as from a server 52. Additionally, one or more collectionprocedures 70 can be provided as part of the software 34, provided asupdates to the software 34, or provide as individual files which can beoperated on and used by the software 34.

In the embodiment discussed hereafter, the software 34 runs on thecollection device 24 and provides three basic elements: one or morestructured collection procedures 70, data file 145, and one or morescripts. As the features of the structured collection procedure 70 anddata file 145 are the same as previously discussed above no furtherdetails is provided. The one or more scripts are small independentprograms that reside on the collection device 24 and each can perform aspecific set of tasks. Such scripts can include a protocol script 401, aparse script 403, and an analysis script 405 such as depicted by FIG.12, each of which are discussed in detail in the following paragraphs.

Protocol Script

The protocol script is a script that actually enables the execution ofthe collection procedure 70 by the processor 102 on the collectiondevice 45. At the time of initiation of collection procedure 70, theprotocol script in one embodiment causes the processor 102 to create adata structure that outlines the amount of data expected as outlined bythe collection procedure 70. In another embodiment, the data structurecan have a variable size, or be a fixed size but with a buffer e.g., anarray in the data structure, should additional data be collected duringthe collection procedure 70. For example, such have buffer can accountfor situations when the collection protocol 70 can be extend, ifdesired, or needs to be extended due to not meeting a desired condition,e.g., a patient biomarker value has not reached a desired value, suchas, for example, up to a maximum size of allocable memory for the datastructure in memory 110 of the collection device 24. This datastructure, such as data file 145, stores at a minimum the time ofinitiation of the collection procedure 70, actual measurement ofbiomarkers, such as data event value 256, and time of the measurements,such as date-time stamp 169, and optionally all other information usedfor additional contextualization, such as the contextual information156, and request 240, such as meals, exercise, etc. As an alternativeembodiment, one can also consider the data structure to be a calendarthat is generated by the clinician 14 which can include details in termsof the day, and the time of day when a measurement needs to be made.This calendar feature also enables the patient to see readily when hehas to make the next measurement. The protocol script also causes theprocessor 102 to perform all of the functions necessary for theprocessor 102 to execute the collection procedure 70. Once appropriatedata is collected, e.g., a successful run of the collection procedure70, the protocol script causes the processor 102 to mark the datastructure with a completion flag 257 in one embodiment or provides it asstate condition of the software 34 in another embodiment and passescontrol of the processor 102 as provided in the software 34 to the parsescript. In the former embodiment, the completion flag 257 can also beused to provide information regarding the reason for ending/terminating,such as to identify the type of completion (end, logistical (timeout),adherence terminated, etc.). For the latter embodiment, as one or morestructured collection procedures 70 may be loaded onto the collectiondevice 24 at the factory as mentioned previously above, providing stateconditions for each collection procedure 70 in the software 34 helps tosupport the requirement that the procedure only be available afterauthorization by the clinician 14. In one embodiment, such stateconditions of each collection procedure 70 can be tracked by thesoftware 34 and can include one or more of a ‘Dormant’ state, an‘Authorized’ state, a ‘Pending’ state, an ‘Active’ state, and a‘Completed’ state. The Dormant state is useful when the collectiondevice 24 is shipped with one or more embedded collection procedures 70,but until authorized for use, such as described above previously, cannotbe use (or seen) by the patient 12 on the collection device 24. In thiscase, the collection procedure 70 is said to be in a Dormant state. TheAuthorized state is when the collection procedure 70 becomes usableafter the clinician 14 authorizes it for use on the collection device24. During this state, the collection procedure 70 can be configured(e.g., by the clinician) and initiated for start as also configured,e.g., via selection by the clinician, the patient 12, or by a startdate. The Pending state is when a start date is set, but prior toexecution, e.g., in which the collection procedure 70 is waiting forsome unknown time until the entry criterion 226 is met before executingthe schedule of events 222. Once the collection procedure 70 beginsexecuting on or after the start date, via meeting the entry criterion226 in one embodiment, the collection procedure is said to be in theActive state in which at least the schedule of events 222 is beingimplemented by the processor 102. The Completed state functions in asimilar manner as to the completion flag 257 when the collectionprocedure 70 has ended as mentioned above previously.

Parse Script

The parse script is the script that causes the processor 102 to parsethe contextualized data, such as e.g., contextualized data 395 (FIG.11), once the collection procedure 70 data collection is complete. Theparse script causes the processor 102 to try to resolve any exceptions(e.g., in real time, i.e., as the procedure 70 is being executed) thatmay have arisen at the time of execution of the collection procedure 70,e.g., for only critical data events 237 in the collection procedure 70(e.g., a mandatory data collection for a biomarker value) in oneembodiment. If at the end of the execution of the parse scriptexceptions remain for at least the mandatory data required for thecollection procedure 70, then the parse script will cause the processor102 to signify that appropriate data has not been collected.Consequently, the collection procedure 70 is marked by the processor 102as incomplete via the completion flag 257 not being provided by theprocessor 102 in the data file 145. If there are no exceptions at theend of a parse script, e.g., at least for the critical events in oneembodiment, and/or events designated as primary samples in anotherembodiment, and/or for all events in still another embodiment, thecollection procedure 70 is marked complete via the processor 102providing the completion flag 257 in data file 145, which contains thecollected and contextualized data. The role of the parse script will beexplained hereafter subsequently in a still another embodimentillustrating an execution stage.

Analysis Script

The analysis script causes the processor 102 to analyze the completedcollection procedures 70 that have their own associated datasets, e.g.data file 145. The analysis performed by processor 102 according to theanalysis script can be simple (mean glucose value, glucose variability,etc.) or it can be more complex (insulin sensitivity, noise assessment,etc.). In one embodiment, the collection device 24 can perform theactual analysis itself, or the analysis can be carried out on acomputer, such as computer 18, 25. In one embodiment, the results fromthe analysis script can then be displayed either on the display 108 ofcollection device 24 by the processor 102 or on the display of aperipheral device. Reference to the scripts and program instructions ofthe software 34 are discussed hereafter with reference made to FIGS. 13and 14 as well as to FIGS. 2 and 5B.

Collection Procedure Execution

FIGS. 13 and 14 depict a collection procedure execution method 400performed by the processor according to the program instructions of thesoftware 34 using the above mentioned scripts during a collectionprocedure 70. The dash-dot lines indicate the boundary between thedifferent domains of the different scripts and are the boundaries acrosswhich exchange in control takes place. It is to be appreciated that thehereafter disclosed embodiments of the present invention can beimplemented on a blood glucose measuring device (such as a meter) thathas the capability to accept one or more structured collectionprocedures 70 and the associated meter-executable scripts discussedabove.

With reference first to FIG. 13, once a collection procedure 70 isinitiated on the collection device 24 by the processor 102 using theprotocol script 401 in step 402, such as in any of the above mannersdiscussed previously above in earlier sections, after meeting entrycriterion 226 (if provided in the collection procedure 70), a data eventinstance, e.g., an event 237, occurs according to the schedule of events222 in step 404. For the event 237, in this example, the processor 102prompts via a request 240 for the patient 12 to take a reading around alunch event as mandated by the collection procedure 70. For example, theprompting of the request 240 may be an alarm provided by the processor102 via indicator 148 that goes off, whereby the patient 12 is askedalso on the display 108 by the processor 102 to take a reading. In oneembodiment, the snooze feature as well as the skip reading feature areprovided by the software 34, where the patient 12 can use the userinterface 146 to enable a delay or to skip the data collection. Forexample, selecting the delay feature as discussed previously above inearlier sections can cause the processor 102 to prompt the patient 12again for the event 237 a predefine amount of time after enabling thedelay to the data collection. For example, such a feature could be usedin case the patient 12 cannot take the reading at the time of theprompting in one embodiment, e.g., at the beginning of the window oftime in which to provide the measurement/reading. Likewise, the skipfeature would be selected if the patient believes he/she cannot performthe measurement/reading within the window of time. An example of awindow of time or a specific time-window around an event is shown byFIG. 10B (“allowable window”).

The processor 102 according to the protocol script 401 then usesadherence criterion 224 in step 406 to determine whether the datacollection for the event 237 was successful by meeting the conditions ofthe adherence criterion 224 in one embodiment. For example, a successfuldata collection will occur if the patient 12 successfully collects thedata within the specified time-window. In another embodiment, the sameprocessing may be applied to one or more sampling group 262.Successfully collected data for such events in the schedule of events222 and/or sampling grouping 262 is then contextualized by processor 102according to the protocol script 401 in step 410, for example, byassociating in the data file 145 with the collected data, e.g., data256, the current time e.g., the date-time stamp 169 (FIG. 4), the event239 and/or request 240, and available contextual information 156, e.g.,about the patient's therapy as well as the unique identifier 167, ifneeded, as discussed also previously above in earlier sections.

If, in the above example, the patient 12 fails to collect data withinthe specified time-window, then in step 412 the processor 102 accordingto the protocol script 401 scans the contextualized data resident on thecollection device 24 to determine if a similar data-point is availablethat meets the requirements of the missed data-point. This data-pointwill be selected by the processor 102 according to the protocol script401 in step 414 only if it fulfils all requirements of the data-pointintended to be collected.

As an example, if the collection procedure 70 requires a pairedmeasurement, i.e., pre- and post-meal measurements, then it is importantfor both of these measurements to be made around the same event. In thiscase, substitution of any one value from a prior value is notpermissible; should such occur, an exception is marked for the eventunder consideration. In this scenario, the pertinent element in thedata-structure is incomplete at that location wherein the processor 102in step 416 will declare an exception, such as providing a <null> valueto the unique identifier 167 in the specific data record 152 for theevent 237 which caused the exception. Should no such constraint exist,then a data-point from the data resident on the collection device 24 canbe selected by the processor 102 in step 414 and added to thecontextualize collected data in step 410. This substitute data-pointwill have the same contextual information, event context, and collectedwithin a specified time-window of the original collection period if suchis a requirement. In step 418, according to the protocol script 401, theprocessor 102 will check to see data collection is completed for all ofthe events 237 in the schedule of events 222 of the collectionprocedure. The processor 102 also checks whether exit criterion 228 ismet, if such is provided by the collection procedure 70. If not, thenthe processor 102 proceeds with the next event in the schedule of events222 by returning to step 404 wherein the data collection then proceedsfor the remainder of the collection procedure 70 in a similar fashion.It is to be appreciated that frequent messages as part of the guidance230 of the collection procedure 70 can be displayed by the processor 102to the patient 12 on the display 108 to guide the patient throughout theentire data collection. It is to be appreciated that as part of theprotocol script that whenever any specified exit criteria is met, theprocessor 102 can end the collection procedure 70 in one embodiment orpresent as an option on the display 108 for the patient to select endingthe procedure 70 in another embodiment. Once the data collection iscompleted in step 418, the protocol script 401 then hands over controlof the processor 102 to the parse script 403 in step 420.

With reference to FIG. 14, which highlights the role played by the parsescript 403, when control is passed upon completion of the collectionprocedure 70, the parse script 403 checks the contextualized data 170 inthe data file 145 for incompleteness. To accomplish this, the processor102 reads the contextualized data 170 from memory 110 in step 422 andlooks for any exceptions (e.g., <null> value for any unique identifier167) provided in the data file 145 as an exception check in step 424according to the parse script 403. When possible, the processor 102tries to address any these exceptions using data available on thecollection device 24 should it be possible in step 426. As an example,applicable data either may be available from non-collection procedure 70events or from data collected as part of another collection procedure70. If the exception cannot be addressed from existing data, then instep 428 the collection procedure 70 is marked incomplete. At this pointthe completion flag 257 for the collection procedure 70 is set asincomplete (e.g., not set, <null>, a pre-define value, etc.). Otherwise,if there are no exceptions and/or all exceptions have been addressed instep 426, then the processor 102 sets the completion flag 257 ascompleted and then can display the result of the collection procedure 70in step 430. The processor 102 in accordance with the parse script 403then collects all of the data associated with the collection procedure70 (i.e., data file 145) and hands control over to the analysis script405 in step 432.

In step 434, the analysis script will cause the processor 102 to performall the necessary analysis, such as analysis 258 (FIG. 6B) that isdetailed in the collection procedures 70, on the data collected in step432, if the completion flag 257 is marked complete in the data file 145.In one embodiment, simple analysis routines calculations can beperformed on the collection device 24, whereas more complex collectionprocedures 70, the analysis can be done on a computer, such as computer18 or 25.

When a collection device 24 containing one or more collection procedures70 is connected to a device reader 22, such as the Smart-Pix device,that is connected to computer 18 or the clinician computer 25, thesoftware 34 cause the associate processor to display automatically alist of the completed collection procedures 70 and their associated datafiles 145.

In one embodiment, the software 34 can interact with the device reader22, such as provided as a SmartPix device, for visualization of results,or with any other device including computer 18, 25, etc., that candisplay the results of the analysis of the data from the collectionprocedure 70. At this point, if on the clinician computer 25, theclinician 14 can decide to view the results of completed and analyzedcollection procedures 70 or carry out analysis of completed collectionprocedures 70. The clinician 14 can also review any collection procedure70 that did not complete and try to evaluate the exceptions that existin the collection procedure 70. This interaction gives the clinician 14an opportunity to give the patient feedback on his data and/or evaluatereasons for the failure to complete existing collection procedure(s) 70.

Use Case Example

Referring to FIG. 15, a use case example is provided which highlights asequence of actions carried out by the clinician 14 as well as thepatient 12. This sequence encompasses an overview of the clinician14-patient 12 interaction from the formulation of the medical questionto the completion of the collection procedure 70. The dash-dot lineindicates the boundary between the clinician 14 and patient 12 domainsand it is the boundary across which information exchange takes place.The discussion on the completed collection procedure 70 also serves toencourage the patient and provides the clinician 14 with an opportunityto provide feedback on patient performance and progress.

In step 440, patient visits the clinician 14 and in step 442, theclinician identifies a problem, which results in the selection ofmedical use case (medical question) in step 444. After selecting themedical question, such as on computer 25, the clinician uses thecomputer to select and define/customize the structured collectionprocedure 70 in step 446 using method 200 and/or 300 (FIGS. 5A and 7A).After prescribing the structured collection procedure 70, the computer25 provides the structured collection procedure 70 to the collectiondevice 24, which is received in step 448. The patient 12 starts the datacollection according to the structured collection procedure 70 using thecollection device 24 after satisfying the entry criterion 226 providedin the procedure 70 in step 450. During the data collection in step 452,events 237 are automatically scheduled by the collection device 24 inaccordance with the scheduled of events 222 contained in the structuredcollection procedure 70. Adherence criterion 224 is applied to at leastto all biomarker measurements, which are evaluated and recorded formeeting the adherence criterion automatically by the collection device24. In step 454, the structured collection procedure 70 is completedonce the exit criterion 228 is met. Next in step 456, any availablecollection device based analysis 258 may be performed by the patient 12if desire. Next in step 458, a report may also be generated, such as thedata report mentioned in step 434 (FIG. 14). In step 460, the data(e.g., the complete data file 145) either from the collection device 24or from the patient computer 18 is preferably sent to the cliniciancomputer 25. The collected data is received in step 460, is thenanalyzed in step 462. Next, in step 464 a report can be generated, whichmay be used to facilitated a discussion of any additional result in step466 with the patient 12. Next, documentation is printed in step 468,which can be given to the patient 12 in step 470 as well as recorded(stored) in an electronic medical record of the patient 12 in step 472.

Generation, Modification, and Transfer of Collection Procedures

Embodiments of the present invention also enable the generation,modification, and transfer of collection procedures 70 to and from astructured testing enabled device, such as collection device 24. As thecollection procedures 70 stem from and aim to address specific medicaluse cases or questions, the transfer of the resultant information e.g.,data file 145, from one device to another is carried out in a securemanner. Additionally, a method whereby all of the collection procedurerelated information (e.g., data file 145) for a patient or a group ofpatients can be managed in a secure and efficient manner.

It is to be appreciated that the discussion provided hereafter includesaspects related to the interaction between the clinician 14 and thepatient 12 as discussed previously above concerning FIG. 15. Inparticular, the disclosure hereafter provides details regarding theinfrastructure required to manage the generation, transfer, and analysisof the collection procedures 70. Reference hereafter is also made to thesystem 41 of FIG. 2, as aspects pertaining to the transfer of devicesand information (data, reports, etc.) to and from the devices 18, 25 and52 are provided.

In one illustrated embodiment, the system 41 can comprise server 52being a web-server that serves as a repository of a plurality ofcollection procedures 70 a, 70 b, 70 c, 70 d, as software 34 thatresides on the clinician computer 25, and the collection device 24, suchas provided as a blood glucose meter. Henceforth these components arereferred to as the “server”, “software”, and the “meter” respectively.Additionally, the computer 25 where the software 34 resides is termed asthe “client”.

In one embodiment, the server 52 can serve as a central repository for anumber of collection procedures 70 a, 70 b, 70 c, and 70 d that addressspecific medical questions. Accordingly, one or more collectionprocedures 70 can be downloaded from the server 52 to the cliniciancomputer 25. In such an embodiment, all communications between theserver 52 and the client computer 25 is done in a secure and web-basedformat. Additionally, in another embodiment, there is no full two-waydata transfer between the computer 25 and the server 52 such thatpatient data can never be transferred to the server 52. Furthermore, inother embodiment, a request for a collection procedure from the server52 can be made only with a valid identifier. Such an embodiment ensuresthat only authorized clients are allowed to access the server 52 todownload the requested collection procedure(s) 70.

In one embodiment, each collection procedure 70 downloaded from theserver 52 can be used only once (e.g., if the completed flag or state isset, the procedure 70 cannot be run again until reauthorized by theclinician 14). Each successive download of the collection procedure 70requires access from an authorized client user with a valid ID 71 (FIG.2). The server 52 also provides the client computer 25 with updatesthereby ensuring that the software is the most recent version. Therealso exist restrictions on the communication from the client computer 25to the server 52. The server 52 can only access information related tothe installed version of the software 34. It is not possible for theserver 52 to access any data resident in the client database e.g.,memory 78. Additionally, the data on the client computer 25 is accesscontrolled so that it cannot be used and accessed without the necessarypermissions.

The software 34 residing on the client computer 25 serves as theinterface between the server 52 and the collection device 24. Thesoftware 34 at the front end includes a user-friendly interface thatprovides the clinician 14 with ready information pertaining to theoverall practice. This information may include details about allassigned patients, details about the patients the clinician 14 isscheduled to see on a given day, as well as the details about patientsthat need extra attention. The software 34 also interfaces with adatabase that includes relevant patient data that is arranged by anindividual patient ID, such as used by and provided in the healthcarerecord system 27. The software interface also allows the clinician 14 toaccess the patient 12 details using the patient identifier. In thismanner the software 34 provides the clinician 14 with information aboutthe collection procedure(s) 70 that the patient 12 has already completed(i.e., those with a completed set for the completion flag 257), theassociated results, and also the collection procedure(s) 70 that thepatient 12 is currently performing. All of the data residing on theclient computer 25 is secure and access-controlled. The server 52 has nomeans to access the data. The clinician 14 can access data from allpatients in the practice. In addition, an individual patient 12 canaccess his data, such as from a server of the clinicians, using hispatient identifier in a secure web-based format. This data is downloadedto the database on computer 25 from the collection device 24 andassociated to the patient 12 using the patient identifier.

At the time of data download from the collection device 24, the software34 also performs an analysis on the data to ensure that the integrity ofthe data is maintained and no corruption in the data has taken place atthe time of transfer. The client computer 25 with the help of thesoftware 34 can also send emails to the individual patients and theseemails can contain information about an upcoming appointment, reminderson what the patient is supposed to do after an appointment and reportsthat are results of a completed collection procedure 70. When theclinician 14 downloads a collection procedure 70 from the server 52 fora particular patient, the collection procedure 70 is associated with thepatient identifier. In this way, it is possible to account for whatcollection procedures 70 are currently underway for his patients.

A downloaded collection procedure 70 can also be modified by theclinician 14 using the software 34 to tailor the collection procedure 70to individual patient needs as previously discussed above in earliersections (FIG. 7B). At the time of modification of the collectionprocedure 70, the clinician 14 also has the option to alter the analysisthat will be carried out on the modified collection procedure 70.Additionally, even for standard collection procedures 70 that have notbeen modified, the clinician 14 has the option to add additional optionsfor analysis.

Furthermore, the clinician 14 can decide and set guidelines as to whenthe procedure 70 must terminate. For example, the clinician 14, candecide and set how many adherence violations are allowed, i.e., how manymeasurements the patient can miss, such as via using the optionsparameter 232 in the collection procedure 70.

In one embodiment, once a collection procedure 70 is introduced into thecollection device 24 by the clinician 14 (details discussed in the nextsection), the collection procedure 70 cannot be altered by the patient12. Additionally, the collection procedure 70 is associated with boththe clinician 14 (the prescriber) and the patient identifiers to ensureaccounting of the collection procedure 70 and associated data (e.g.,data file 145).

The software 34 also allows the clinician 14 to select the type ofreport that will be generated once the completed collection procedure 70has been analyzed. This report is tailored for the device on which itwill be viewed. The report could be for a mobile device such as atelephone, a palm device or a meter, or a computer, or a printed format.The software 34 also has the ability to connect with an electronicmedical records system to add patient data and results of analysisperformed on the data from a collection procedure 70 to the medicalrecords.

The collection device 24 serves as the mechanism by which prospectiveand contextualized data is collected by the patient 12 as recommended bythe collection procedure 70. The collection device 24 can be owned bythe patient or it can be owned by the clinician 14 and loaned to thepatient 12 for the duration of the data collection associated with thecollection procedure 70. The clinician 14 can introduce the collectionprocedure 70 into the collection device 24 by a number of mechanisms.For example, the collection procedure 70 can be downloaded from theserver 52 and added to the collection device 24 via a connecting cablethat links the client computer 25 to the collection device 24 in oneembodiment. The collection procedure 70 can also be obtained in anotherembodiment on a chip (e.g., computer readable medium 40) that can beinserted into the collection device 24. This collection procedure 70 isthen loaded into firmware of the collection device 24 where it can beinitiated by the patient 12. The collection procedure 70 can also beintroduced using an RFID tagged chip (e.g., computer readable medium) instill another embodiment.

Along with the downloaded collection procedure 70, the collection device24 also has the ability to display instructions to the patient 12 thatguide the patient at the time of data collection. Additionally, asdiscussed above, the collection procedure 70 can introduce into thecollection device 24 both the patient identifiers as well as theclinician identifier. Similarly, the data collected from the collectiondevice 24 can be associated with the patient identifier and clinicianidentifier, such as part of setup data 163 (FIG. 4) in the data file145. Additionally, the setup data 163 in the data file 145 can includeinformation about the collection device 24 (i.e., measurement noise,calibration data), as well as strip lot numbers and other informationabout the strips used for any data collection event 237. Suchinformation may be helpful at the time of data analysis.

At the completion of the collection procedure 70 the collection device24 can be connected to the software 34. At that time data, such as datafile 145, is transferred securely and stored by the processor 76 of theclient computer 25 according to the software 34 running thereon. Oncethe analysis performed on the data from the collection procedure iscompleted by the software 34 on the client computer 25, the computer 25also has the ability to store results of the analysis for patientreference. Reference is now made to FIGS. 16-18 hereafter.

Software GUI

In one embodiment, a typical workflow highlighting further features ofthe software 34 useable through a graphical user interface (GUI 500)provided thereby on a computer, such as computer 25 and/or server 52,are presented. In this example, the typical scenario is when theclinician 14 opens the case file for a particular patient is considered.As shown in FIG. 16, the clinician 14 can readily visualize importantdetails about a displayed patient file 502 using the GUI 500 of thesoftware 34 running on the client computer 25. On a top pane 502 of theGUI 500, the clinician 14 can see and use various administrative tasks504, such as changing the displayed patient file, create an emailcontaining information form the patient file, create a fax containinginformation from the patient file, save the patient file, bookmarkingdata in the patient file, select existing bookmarks, printinformation/graphs from the patient file, etc.

On a left pane 506 of the GUI 500, the clinician 14 has additionaloptions 508 such as the option to download patient data, such as datafile 145, when a collection device 24 is connected to the computer 25 or18 (wired or wirelessly). The other options 508 also can also includeviewing details regarding a patient profile, logbook, and additionalrecords, and graphs based on calculated data, etc. As indicated by FIG.16, the summary option is selected, which shows its content in a mainpane 510.

The main pane 510 indicates all of the typical steps in a workflow fortherapy administration for the patient 12. These steps can include thefollowing: Disease State 512, Therapy Selection 514, TherapyInitialization 516, Therapy Optimization 518, and Therapy Monitoring520. Each step provided as an icon on the GUI 500 is discussedhereafter.

Disease State 512 is a determination of the disease state, e.g., thepatient is a Type 1 or a Type 2 diabetic. Typically, the disease statedetermination is carried out when the patient 12 first visits theclinician 14 or when the clinician 14 suspects that a particular patientmight be at risk. Therapy Selection 514 follows thereafter once thedisease state is determined, and the clinician 14 needs to select anappropriate therapy that takes into account the patient's disease state.As Therapy selection 514 can include the processes of methods 200 and300 shown by FIGS. 5A and 7A, respectively, no further discussion isprovided. Therapy Initialization 516 is the process of therapyinitialization involves establishing the initial details by means ofwhich therapy is administered to the patient 12. This may includedetails about the starting dose of the therapy, time when thetherapeutic is taken, and the likes. Further details about TherapyInitialization 516 are provided hereafter in reference to FIG. 17.Therapy Optimization 518 involves the determination of the besteffective dose for the patient such that it will not cause side effects.Finally, Therapy Monitoring 520 involves routinely monitoring thepatient 12 to detect therapy obsolescence after the selected therapy hasbeen optimized. Thus, the GUI 500 provides the clinician 14 with all ofthe useful information in a user-friendly format.

FIG. 17 represents the scenario when the clinician 14 has alreadydetermined the disease state and selected a therapy, via Disease State512 and Therapy Selection 514, and is at the step for TherapyInitialization 516. As shown, the software 34 shades in the GUI 500 thesteps already completed wherein only the step currently underway, e.g.,Therapy Initialization 516, is highlighted. In addition, in oneembodiment, the software 34 does not permit the clinician 14 to progressto the next step without accomplishing all the required actions in thecurrent step (in other words, all previous steps have beenaccomplished). However, the software 34 provides the clinician 14 withthe option to go back and modify prior steps via selecting theparticular icon for the step in the GUI 500.

In this example, the patient 12 is diabetic, and currently for TherapyInitialization 516 the clinician 14 needs to initialize a long actinginsulin therapy for a Type 1 diabetic patient. As shown, the clinician14 is presented on the GUI 500 for this step with all availableinitializing options 522 for initializing the therapy. For example andas shown, the clinician 14 can select a type of drug 524, such as showas a long acting basal insulin, and select procedure selection icons 526associated with the drug 524 and each associated with a collectionprocedure 70 that is available for addressing a therapy question(s)regarding a particular drug (e.g., Lantus, Levemir) listed associated(and available) with the type of drug 524. The software 34 through theGUI 500 also permits the clinician 14 to decide if additional therapyrelated parameters 528, such as insulin sensitivity, insulin tocarbohydrate ratio, and the like, should be undertaken if such isneeded. Additionally, further details for the therapy initialization canbe viewed via selecting an icon for general information 530.

When the clinician selects one of the procedure selection icons 526, thesoftware 34 provides a snapshot 532 of the conditions set in theassociated collection procedure 70, such as illustrated by FIG. 18.Typical initial conditions provided in the snapshot 532 can include:frequency of dosage (dosing adjustment), (default) starting dose, targetlevels, schedule of events (e.g., Measure fasting blood glucose for 3days), recommendations for computation (e.g., modify drug dose base onthe 3-day median, measure remaining for days to assess the effect), andthe like. If more details regarding the selected collection procedure 70are desired, such as related medical literature, case studies that mayhave formed the basis for the structured testing procedure, and thelike, can be viewed via More Detail icon 534. The clinician 14 has alsothe choice to either accept the collection procedure 70 as provided, viathe Accept icon 536 or suggest modifications to the collection procedure70 via the Modify protocol 538. As selecting the Modify protocol 538 canopen on the GUI 500, for example, a screen representation of all theparameters in the procedure 70 for modification, such as depicted byFIG. 7B, and as such was previously discussed above in earlier sectionsno further discussion is provided. Once modifications are made to thecollection procedure 70, the clinician 14 can review and accept thechanges. Upon accepting the collection procedure 70, via selecting theAccept icon 536 on the GUI 500, the software 34 cause the processore.g., processor 76, to send the completed collection procedure 70 to thecollection device 24 as discussed previously above in earlier sections.Certain advantages of the above mentioned embodiments of the presentinvention are noted hereafter.

Although not limited thereto, the embodiments of the present methodoffer the following noted advantages. Certain embodiments enablecontextualization of collected data by taking into account factors suchas meals and existing medications. All of the data analysis can becarried out on prospective data, i.e., contextualized data collection iscarried out keeping in mind the medical question that needs to beaddressed. The collection procedures 70 are each geared towardscollecting bG data to address a specific medical issue, e.g., control ofpostprandial glycemic excursions, regulating the fasting blood glucosevalue, characterizing the patient insulin sensitivity, monitoring thepatient's therapeutic response, and the like. Using such collectionprocedures, makes the task of collecting BG values goal oriented as thepatient knows the reason why he or she is carrying out such tests. It isbelieved that awareness of the reason for conducting tests would lead toan increase in adherence.

Also, certain embodiments provides the infrastructure necessary tomanage multiple simultaneously running collection procedures 70 ondifferent collection device 24 by different patients 12, while ensuringsecure web-based communication for receiving and transmitting thecollection procedures 70 and the results obtained from the analysis ofthese collection procedures 70. For example, certain embodiments helpthe clinician 14 by: making it easier for the clinician 14 to impact allof the stages of a patient's therapy ranging from disease statedetermination to regular monitoring under a working regular therapy;making it possible for the clinician 14 to manage the various stages ofcollection procedure 70 execution for a group of patients in a secure,and web-based format; offering the clinician 14 flexibility by providingthe option to select collection procedures 70 from a pre-determined listor modify a collection procedure 70 based on patient needs; making theinteraction between the clinician 14 and the patient 12 more effectiveas the communication is entirely data-centric and guided by, forexample, a medical question at hand.

Specific examples of therapy optimization are described below,specifically in the context of insulin titration.

Structured Collection Embodiments for Optimizing the Titration ofInsulin

FIG. 19A provides an exemplary embodiment of structured collectionprotocols for optimizing the titration of insulin dosage, which therebyyield dosages of insulin which maintain biomarker levels within adesired range. In one embodiment, the titrated insulin may be basalinsulin. Upon starting the structured collection, the dosage of insulinis typically the initial prescribed dosage, for example, the initialprescribed dosage listed on the package. However, other dosages arecontemplated depending on what stage of the structured collectionprotocol, as the entry criteria may be considered before every biomarkerreading. Consequently, the initial dosage may be an adjusted dosageabove the initial prescribed dosage, the maximum allowable dosage, oreven the optimized dosage. It is contemplated that the structuredcollection may be used to obtain the optimized insulin value, or may beused post-optimization to verify that the insulin dosage is stilloptimal.

In the embodiments of FIG. 19A, the structured collection protocols mayoptionally require the consideration of entry criteria 710 beforebeginning collection of the biomarker data. It is contemplated that thediabetic person, the healthcare provider, or both may determine whetherthe entry criteria are met. The entry criteria, which in someembodiments may be established by the healthcare provider, may relate tothe age, weight, and medical history of the diabetic person.Consequently, the structured collection protocol may require a diabeticperson to receive a check-up or physical to ensure the diabetic personsatisfies the entry criteria. For instance, the entry criteria mayspecify the fasting plasma glucose (FPG) level or glycolated hemoglobinlevel as determined by the HbA1c test. The normal range for the HbA1ctest is between 4-6% for people without diabetes, so the entry criteriamay require values above about 6%, or in exemplary embodiment, betweenabout 7.5% to about 10%. As an additional example of entry criteria, afasting plasma glucose level of at least about 140 mg/dl is required.The entry criteria may also set requirements on weight or Body MassIndex (BMI). For example, the required BMI may be greater than about 25kg/m2, or between about 26 kg/m2 to about 40 kg/m2. Additionally, theentry criteria may specify a desired age range (e.g., 30-70) or thenumber of years afflicted with diabetes (e.g., at least 2 years).Moreover, while it is contemplated that the structured collectionprotocol is applicable to persons afflicted all types of diabetes, theentry criteria may limit the structured collection protocol to type 2diabetics. Furthermore, the entry criteria may center on the currentdiabetes treatment regimen of the diabetic person. For example, theentry criteria may require that the treatment regimen for the diabeticperson be limited to oral anti-diabetes medication i.e., no injectedinsulin. Additionally, the entry criteria may require that the diabeticperson not be ill or under stress. As stated above, while theembodiments of FIG. 19A are directed to the consideration of entrycriteria, the present structured collection protocols do not require theconsideration of entry criteria before collection of biomarker data. Forexample, referring to the additional embodiments of FIGS. 19B-D, theembodiment of FIG. 19B requires the consideration of entry criteria;however, the embodiments of FIGS. 19C and 19D do not include suchconstraints.

Referring again to FIG. 19A, if the entry criteria are not met, thestructured collection protocol will not be initiated 715. The diabeticperson or healthcare provider may determine whether the entry criteriahave been met, or the data processor may determine whether criteria havebeen met. If the entry criteria are met 710, then the diabetic personmay commence with the structured collection protocol. However, in someembodiments, it may be required for the diabetic person satisfyadherence criteria before the collection of biomarkers or theadministration of insulin.

The adherence criteria are the procedural requirements that the diabeticperson must follow when conducting the structured collection protocol.To get a proper baseline for the biomarker readings, it may bebeneficial to ensure all readings are taken uniformly, i.e., atapproximately the same time of day for each sampling instance.Consequently, the adherence criteria may specify that biomarkercollection or insulin administration be conducted at the same time eachday. To aid the diabetic person in satisfying the adherence criteria,the data processor display prompt the diabetic patient with audio and/orvisual reminders to collect their biomarker sample, and enable thediabetic patient to set future reminders. In specific embodiments, theadherence criteria may also require that the diabetic person fast for aset period of time prior to collecting the biomarker reading. Theadherence criteria may also be directed to determining whether thediabetic person is taking the correct dosage of insulin. In additionalembodiments, the adherence criteria may also require no recenthypoglycemic events or severe hypoglycemic events (low blood glucoseevents) a set period (e.g. one week) before the collection of biomarkerdata. Furthermore, the adherence criteria may specify an exerciseregimen or eating regimen for the diabetic person. As used herein,“eating regimen” means the typical eating regimen of the diabetic personin terms of calories, carbohydrate intake and protein intake.

If the diabetic person fails to meet any or all of the adherencecriteria, the diabetic person may be informed, for example, by thedisplay of the blood glucose meter that they failed to meet theadherence criterion. If the diabetic person fails to meet the adherencecriteria, the data processor device may tag the adherence event or thediabetic person may record the occurrence of the adherence event. Afterthe adherence event is recorded, the structured collection protocol istypically continued. However, if too many adherence events are recorded(e.g., more than 4 within a sample period, more than 20 adherence eventswithin the entirety of execution), then the structured collectionprotocol may be terminated. Furthermore, the structured collectionprotocol may also evaluate adherence events differently. For example,there may be a tiered adherence event assessment, wherein adherenceevents are weighted. In one or more embodiments, if the adherence eventdoes not impact the biomarker data, then it is not weighted as heavilyas an adherence event that affects the biomarker data. For example, whena diabetic person fasts the requisite time period before taking afasting blood glucose reading, but fails to record that the reading is afasting blood glucose reading, this would be categorized diabetic as aless significant and thereby lower weighted adherence event, because therecording error does not affect the fasting blood glucose reading. Incontrast, fasting less than the requisite period will impact the fastingblood glucose reading, and thus constitutes a more significant andthereby higher weighted adherence event.

If there is a violation event (e.g., a missed insulin administration),the structured collection protocol is more likely to be terminated thanfor an adherence event (e.g., fasting less than the required fastingperiod), because a violation event impacts the structured collectionprotocol more significantly. Since the present structured collectionprotocol is directed to optimizing insulin administration, it stands toreason that missing an insulin dose would be a significant violationevent.

Like other instructions provided to the diabetic person throughout thestructured collection protocol, the entry criteria or the adherencecriteria may be provided to the diabetic person via a paper instructionform, or a display unit on a data processing device or processor 102 asshown in FIG. 3. The data processing devices may be any electronicdevice described above. In one or more embodiments, the data processingdevice may be a computer or a blood glucose meter with a data processorand memory units therein. In addition to listing the entry criteria,adherence criteria, or both, the data processing device may prompt thediabetic person to answer medical questions, wherein the answers to themedical questions are used by the device to determine compliance withthe entry criteria, or adherence criteria. The data processing devicemay inform the diabetic person of the failure to comply with the entrycriteria or adherence criteria. For example, the data processing devicemay inform a diabetic person if subsequent sampling instances are nottaken around the same time as the first sampling instance. The patientcan record sampling instances or answer medical questions by enteringthe data event directly into a device or computer, wherein the processor102 can store the information and provide additional analysis dependingon the parameters of the structured collection protocol.

Referring again to FIG. 19A, the diabetic person may begin collection ofone or more sampling sets of biomarker data. Each sampling set comprisesa sufficient plurality of non-adverse sampling instances recorded over acollection period, which means at least two sampling instances which arenot indicative of an adverse event e.g., a hypoglycemic or hyperglycemicevent. Each sampling instance 740 comprises a biomarker reading at asingle point in time. The collection period for the sampling set may bedefined as multiple sampling instances within a day, multiple samplinginstances within a week, multiple sampling instances within consecutiveweeks, or multiple sampling instances on consecutive days within a week.The biomarker may relate to the levels of glucose, triglycerides, lowdensity lipids, and high density lipids. In one exemplary embodiment,the biomarker reading is a blood glucose reading. In addition to thebiomarker reading, each sampling instance may comprise the biomarkerreading and other contextual data associated with the biomarker reading,wherein the contextual data is selected from the group consisting of thetime of collection, the date of collection, the time when the last mealwas consumed, affirmation that fasting has occurred for the requisiteperiod, and combinations thereof. In the exemplary embodiment of FIG.19B, the structured collection protocol occurs over a 7 day method whichrequires the diabetic patient to administer insulin in the eveningfollowed by the collection of fasting blood glucose reading thefollowing morning. In addition to the morning biomarker collection, thediabetic patient may also be instructed to take an additional biomarkerreading when the diabetic person is encountering the symptoms ofhypoglycemia.

Referring again to FIG. 19A, upon collecting the biomarker reading,there is a determination as to whether the biomarker reading indicatesan adverse event 750. While the present discussion of adverse eventscenters on hypoglycemic events and severe hypoglycemic events, which maynecessitate medical assistance, it is contemplated that the adverseevents may refer to undesirable levels of other biomarkers or medicalindicators, e.g., lipids levels, blood pressure levels, etc. In oneembodiment, this determination of adverse events may be performed bycomparing the biomarker reading to a low threshold, for example, thehypoglycemic event or severe hypoglycemic event thresholds shown inTable 1 below. If the biomarker reading is below one or both of thesethresholds, then an adverse event may have occurred, and should berecorded as an adverse event, or specifically recorded as a hypoglycemicevent or severe hypoglycemic event. As described above, thisdetermination may be performed by a data processor unit, or may beentered manually by the diabetic person.

TABLE 1 Insulin Adjustment Parameter Blood Glucose Range (mg/dl) (units)below 56 (severe hypoglycemic event) −2 to −4 56-72 (hypoglycemic event)0 73 to 100 (target biomarker range) 0 100-119 +2 120-139 +4 140-179 +6180 and above +8

If there is an adverse event (e.g., a severe hypoglycemic event), in oneembodiment, the instructions or data processing device may recommendthat the diabetic person contacts their health care provider. In anotherembodiment, the system may automatically contact the health careprovider (HCP). In addition, an adverse event may optionally lead to adosage reduction. Referring to Table 1 above, if it is a hypoglycemicevent (between 56-72 mg/dl), the HCP may be contacted 850, but thedosage is not adjusted (See FIG. 19A). However, if it is a severehypoglycemic event (below 56 mg/dl), the dosage may be reduced by someamount (640), for example, 2 units, 4 units, or another amount asdictated by the low biomarker reading. In specific embodiments, if therecorded adverse event is a second measured severe hypoglycemic eventwithin the same day, the dosage is not reduced. In further embodiments,a data processing device may utilize an algorithm to automaticallyreduce the insulin dosage and instruct the diabetic person of thereduced insulin dosage. Moreover, the data processing device whichcollects the biomarker reading may automatically notify a healthcareprovider of the adverse event, for example, by an automated email ortext message.

If the biomarker reading is not adverse, the next step depends onwhether or not the sampling set 760 has a sufficient number ofnon-adverse sampling instances. If only one sampling instance isrequired for the sampling set, then the biomarker sampling parameter maybe calculated at that point; however, as stated above, the sampling settypically requires a plurality or at least two sampling instances foreach sampling set. In exemplary embodiments, two or more samplinginstances taken on consecutive days are required for each sampling set.If multiple sampling instances are required, then the diabetic personmust continue to collect sampling instances.

Once the requisite number of sampling instances for the sampling set isobtained, the biomarker sampling parameter may be obtained 770. Thebiomarker sampling parameter may be determined by various algorithms ormethodologies. For example, it may be determined by averaging samplinginstances, summing the sampling instances, performing a graphicalanalysis on the sampling instances, performing a mathematical algorithmon the sampling set, or combinations thereof. In an exemplaryembodiment, sampling instances (i.e., biomarker readings) are collectedon at least three consecutive days, and the average of the threeconsecutive days is the biomarker sampling parameter.

After the biomarker sampling parameter is obtained, the value iscompared to a target biomarker range. As used herein, the targetbiomarker range means an acceptable range of biomarker in the diabeticperson, which thereby demonstrates that the insulin is producing thedesired physiological response. If the biomarker sampling parameterfalls outside of the target biomarker range, then an insulin adjustmentparameter may be calculated 790. The insulin adjustment parameter isassociated with and computed from the biomarker sampling parameter.Various methodologies and algorithms are contemplated for calculatingthe insulin adjustment parameter. For example, the insulin adjustmentparameter may be computed by locating the insulin adjustment parameterassociated with the biomarker parameter in an insulin adjustmentparameter lookup table (See Table 1 above). As shown above in theexemplary insulin adjustment parameter lookup table of Table 1, theremay be multiple tiers which dictate how much the insulin dosage shouldbe adjusted. For example, a fasting glucose level below 100 mg/dl butabove 56 mg/dl will necessitate no adjustment to the insulin dosage. Thegreater the deviation from the target range, the higher the adjustmentof insulin in units.

After determining the insulin adjustment parameter, the insulin dosagemay be adjusted by the amount of the insulin adjustment parameter, aslong as the insulin adjustment does not raise the insulin dosage abovethe maximum allowable dosage. The adjusted insulin dosage cannot exceeda maximum level set by the healthcare provider. Upon determining theadjusted insulin dosage value, the diabetic person may then beinstructed to collect at least one additional sampling set at theadjusted insulin dosage per the above described collection procedures.The biomarker sampling parameter, the insulin adjustment parameter, andthe adjusted insulin dosage may be computed manually by the diabeticperson or via a data processing device.

If the biomarker sampling parameter is within a target biomarker range,there is no adjustment of the insulin dosage. Moreover, the insulindosage may be considered optimized depending on other applicablecriteria. Specifically, an insulin dosage may be considered optimized ifone biomarker sampling parameter is within a target biomarker range, orit may be considered optimized if at least two consecutive biomarkersampling parameters are within a target biomarker range 820. If theoptimization definition requires at least two consecutive biomarkersampling parameters within a target biomarker range, the diabetic personis then instructed to collect at least one additional sampling set atthe adjusted insulin dosage per the above described collectionprocedures. After the insulin dosage is considered optimized, thediabetic person is instructed to exit the structured collectionprotocol. After exiting the structured collection protocol 730, thediabetic person may conduct further structured collection protocols todetermine the future efficacy of the optimized dosage.

In alternative embodiments, the diabetic patient may be instructed toexit the structured collection protocol 730 if the diabetic person hasbeen undergoing the testing procedure for a long period, for example, 6months or longer. Additionally, as described above, if there aremultiple adherence or violation events, then the test may beautomatically terminated by the data processing device or the diabeticpatient may be instructed to exit the structured collection protocol.

Classification Based on Adherence Criteria

In further embodiments, the structured collection procedures or protocolmay utilize classifications of sampling instances in a sampling set inorder to optimize the structured collection protocol. As describedabove, the structured collection protocol may comprise variouscalculations directed to assessment of a disease or therapy, theoptimization of a therapy, or combinations thereof. In one embodiment,the structured collection protocol may be the insulin titration andoptimization processes described above. The structured collectionprotocol may be performed on a collection device as described above,wherein the collection device may comprise a meter configured to measureone or more selected biomarkers, a processor disposed inside the meterand coupled to memory, wherein the memory comprises collectionprocedures, and software for the processor to execute.

Referring to FIG. 20, the user or diabetic patient begins the structuredcollection protocol 900 by collecting one or more sampling sets ofbiomarker data using the collection device 905, wherein each samplingset comprises a sufficient plurality of sampling instances recorded overa collection period. Each sampling instance includes a biomarkerreading. In one embodiment, the processor may require the samplinginstances to be non-adverse i.e., there are no biomarker readingsindicative of hypoglycemia or hyperglycemia. For each sample, theprocessor may determine whether there is compliance with adherencecriteria 915. Noncompliance with adherence criteria is recorded as anadherence event.

Referring to FIG. 20, either before or after collection, each samplinginstance may be classified using the processor 920, 930. The samples maybe classified as primary or secondary samples. Primary samples 925 aresampling instances intended to be utilized preferably in calculationsperformed by the processor that yield therapy results (e.g., adjustedinsulin dosage amounts) for a diabetic person. As used herein, “therapyresults” are metrics, data, contextual information yielded from thecalculations performed in the structured collection protocol, whereinthe structured collection protocol is directed to disease statusassessment, therapy assessment, therapy optimization (e.g., insulintitration), or combinations thereof. As described above, primary samplesmay be construed as critical events, which should not be missed whenconducting the structured testing protocol; however, they can bereplaced with other samples. Secondary samples preferably arenon-critical sampling instances not intended to be utilized incalculations performed by the processor that yield therapy results for adiabetic person 930, 940 unless the secondary samples are promoted toprimary samples as described below. Regardless of whether samples areclassified as primary or secondary, all sampling instances may beutilized in identifying adverse events, or measurement issues associatedwith the biomarker readings.

In further embodiments, it is contemplated to have additionalclassifications. For example, sampling instances may be classified astertiary samples. Tertiary samples 935 are samples which cannot bepromoted to primary samples as a replacement for another primary sample.While adherence is considered for tertiary samples, these values have noimpact on the structured collection protocol. For example, but not byway of limitation, tertiary samples are sampling instances used forsafety checks, as spacers to maintain a consistent sampling interval, oras input to visual presentations of reported data. It is contemplated tohave further classifications in addition to primary, secondary, andtertiary. Like the primary and secondary samples, the tertiary samplesmay still be utilized to identify adverse events.

Referring to FIG. 20, for primary samples without corresponding recordedadherence events, the user may simply continue the protocol 955, oroptionally determine if there are a sufficient number of primary samplesin the sampling set, which have complied with adherence criterion.

For primary samples with corresponding recorded adherence events 945,the processor may determine which further action 960 or additional taskis appropriate. While it is contemplated to terminate the protocol inlight of adherence events, the embodiments of the present inventionfocus on compensating for these adherence events while continuing theperformance of the structured collection protocol. The processorincludes algorithms that calculate which additional task should beperformed based on numerous factors. The algorithm may consider thecontextual information for the sampling instances, the number ofsampling instances, the classification of the sampling instances, theadherence of the sampling instances, and various other contemplatedfactors. Based on these factors, the algorithm selects the additionaltask most suitable for continuing the structured collection protocol

In one embodiment, the processor may consider whether the sampling sethas the requisite number of primary samples 965, without correspondingrecorded adherence events. If so, the processor may perform theadditional task of ignoring these primary samples with correspondingrecorded adherence events and performing the calculations using primarysamples, which do not include corresponding recorded adherence events,in order to obtain therapy results 985. Referring to FIG. 20, if thedesired results of the protocol are achieved 990 (e.g., the insulindosage is optimized), the user may be instructed to end the protocol970.

Alternatively, if there are not a sufficient number of primary sampleswithout recorded adherence events in the sampling set, the collectiondevice may consider whether there are secondary samples which aresuitable replacements for the primary sample 995. In this case, if thereare secondary samples, which do not have corresponding recordedadherence events, these secondary samples may be promoted to primarysamples and may replace the primary samples with associated adherenceevents 980. In another exemplary embodiment, the replacement of theprimary samples may be performed automatically via the processor. Whenreplacing a primary sample with a secondary sample, the processor mayrequire that the primary sample and the secondary sample be part of thesame sampling set. Moreover, when replacing a primary sample with asecondary sample, the processor may also require that the secondarysample comprise the same, substantially identical, partially identicalor similar contextual information or metric as the primary sample to bereplaced. For example, the processor may replace a primary fasting bloodglucose level with a secondary fasting blood glucose level since theprimary and secondary samples would have the same metric. In contrast,the processor would not replace a primary fasting blood glucose levelwith a secondary insulin to carbohydrate reading because the metricsi.e. contextual information is not the same. Unlike primary samples,adherence events for secondary samples may not require any furtheraction or additional tasks to be performed (e.g. replacement); however,recorded adherence events may render a secondary sample unsuitable for areplacement for a primary sample 950. If there are no suitablereplacement samples, for example, due to adherence events, thecollection device may instruct the user to collect at least oneadditional sampling instance 1000 to be used as a primary sample in thestructured collection protocol. In another embodiment, the processor mayinstruct the diabetic person to restart the sampling set 1005, withoutfirst terminating the protocol.

While the embodiments of the present invention are primarily focused onextending the structured collection protocol to compensate for primarysamples with adherence events, the processor may, in some embodiments,instruct the user to end the protocol 970, because of the adherenceevent. Alternatively, the user may be instructed to contact a healthcareprovider 975. It is contemplated that the user may be instructed to bothend the protocol and contact the healthcare provider.

To reiterate, the at least one additional task is calculated by theprocessor if one or more sampling instances is recorded with acorresponding recorded adherence event and the sampling instance isclassified as a primary sample. For example, if an adherence event isrecorded for a primary sample, the system may calculate a resultingtask, for example, replacing the primary sample by a secondary samplewhich fulfills the adherence criteria. As a consequence, the secondarysample is promoted as a primary sample.

However, if an adherence event for a secondary sample is recorded, whichmight be the same adherence event as recorded for the primary sample asexemplarily mentioned above, the resulting consequence that iscalculated by the system may be different. For example, the resultingtask may be to block that secondary sampling instance from furthercalculation, and thereby preclude this secondary sample from being usedas a primary sample.

By dynamically calculating a task resulting from an adherence event, thesystem facilitates the flexible handling of the structured collectionprotocol. In particular, in case of an incorrect handling of theprotocol by the user or a detection of undesired value, the systemallows continuing the protocol so that the stored data is not wasted andcan be evaluated within the structured collection protocol. Based on oneor more embodiments, a differentiation between different classificationsof sampling instances is possible so that an adherence event can beflexibly handled by the system. For example, adherence events forprimary samples should not be used in the calculations of the structuredcollection protocol. However, adherence events for secondary samples areprimarily utilized in determining whether a secondary sample is suitableas a primary sample, and do not require replacement of the secondarysample in the sampling set. Moreover, adherence events for tertiarysamples are recorded; however, the tertiary samples are not utilized inthe calculations of the structured collection protocol.

The above described embodiments of the classification centered onadherence generally; however, the embodiments of the present inventionmay also encompass acceptance criteria, which are defined above as asubset of adherence criteria. In one embodiment, the acceptance criteriamay require a biomarker reading to be within an expected range. In someinstances, a biomarker reading outside of the expected range may beindicative of an adverse event. It is contemplated that the structuredcollection protocol may compensate for this adverse event by utilizingthe procedures illustrated in FIG. 20.

Structured Collection Data Redundancy and Compression

Referring again to FIG. 2, various patient data may be collected by acollection device 24 following structured collection procedures 70. Uponbeing collected, the patient data may be written to at least one memorycommunicably coupled to the collection device 24. It is noted that theterm “communicably coupled,” as used herein, means an exchange of datavia a communication medium such as for example, electrical transmissionover a conductive medium, electromagnetic transmissions over the air,optical transmissions over a waveguide, and the like. Thus, as depictedin FIG. 3, patient data may be written to the main memory 110 of thecollection device 24, a secondary memory 112, a computer readablemedium, a network database and/or any other storage medium communicablycoupled to the collection device 24.

The patient data written to the at least one memory may be accessed by aphysician to administer and/or evaluate therapy, as is described ingreater detail herein. Since certain patient data may alter the therapyadministered to a patient, it may be desirable to have at least onememory where certain data is duplicated for system redundancy in orderto increase the reliability of data retrieval. For example, a fullyredundant system wherein all patient data is duplicated complies withmedical device standard EN-IEC 60601. It is noted that duplicating datahas specific utility and may be useful to increase the probability ofretrieving patient data after a memory error. Duplicate data may alsoincrease the probability of data anomalies and corruption which mayultimately cause the loss of patient data. Furthermore, any duplicationof patient data also results in an increased consumption of memory.Thus, any memory that duplicates data may waste memory space and may benon-optimal.

Data segregation is a logical concept that may be implemented throughvarious logical and physical constructs to facilitate full or partialdata redundancy. Logical segregation controls the quantity ofaddressable segments of data that are accessible (e.g., for writing,reading, overwriting or deleting) by processes executed by a processorcommunicably coupled to the at least one memory such as, for example,the processor 102 of the collection device 24. Thus, patient data may bedivided into distinct data stores where access to and from the datastore is controlled by logical segregation. The patient data may besegregated such that the patient data is written to distinct physicalmodules of the at least one memory corresponding to each of the datastores (i.e., written to separate hardware devices). Patient data may besegregated such that the patient data is written to data stores within asingle hardware device of the at least one memory. Moreover, patientdata may be segregated by a combined scheme where patient data iswritten to distinct data stores such that at least one of the datastores is maintained on a single hardware device and a multiplicity ofthe distinct data stores is maintained on a single hardware device.

By employing multiple distinct data stores, the storage requirements fordata redundancy may be decreased through a data approach, an informationapproach and/or compression. A data approach may be implemented, whereinall data associated with a result is duplicated and/or retained whileunassociated data is omitted from duplication and/or storage. Aninformation approach may also be implemented, wherein only the datanecessary to reconstruct a result and/or the data necessary to generatethe result is duplicated and/or retained. Specifically, a dataabstraction may be generated to operate as a link to a result and/or thedata necessary to generate the result. The data abstraction may be atransformation of the result and/or the data necessary to generate theresult that is an indirect representation of the data such as, forexample, a unique identifier, a pointer or a combination thereof.

The data abstraction may also be the result of a transformation of theresult and/or the data necessary to generate the result into acompressed data, i.e., transforming a data instance by a datacompression algorithm such as, for example, a lossless data compressionalgorithm or a lossy data compression algorithm. The memory required tostore data residing in the multiple distinct data stores can also bereduced utilizing a data compression algorithm. Therefore, storagerequirements may be reduced via omission, reference, compression, andcombinations thereof. It is noted that, as used herein, the terms“transform” and “transformation” refer to a change in form or structureof an input value, wherein the input persists or does not persist.

Furthermore, data abstractions may be utilized to link partial data setsto complete data sets in order to achieve full redundancy throughpartial duplication. Specifically, patient data may be collected in acasual manner (e.g., bG data, carbohydrates associated with meals, andthe like) and in a structured manner (e.g., via a structured collectionprocedure). The casual patient data may be written substantiallycomprehensively in a primary data store. Structured patient data may bewritten to a secondary data store that is segregated from the primarydata store. When the structured patient data is a subset of the casualpatient data, a functional equivalent to a fully redundant system may becreated by associating the casual patient data and the structuredpatient data to a data abstraction. The data abstraction may be, forexample, written to the primary data store and/or the secondary datastore and associated with the casual patient data and the structuredpatient data in order to link the casual patient data with thestructured patient data.

Structured patient data may be transformed into an evaluated data objectin order to facilitate the generation of a data abstraction.Specifically, structured patient data may be evaluated in order todetermine if the structured patient data is to be reduced via omission,reference, compression, or combinations thereof. For example, theevaluated data object may be a classification (e.g., primary sample,secondary sample, or a tertiary sample) of the structured patient data,as is described hereinabove. Thus, the structured patient dataclassified as primary samples may be written to a data store differentlythan secondary samples and tertiary samples. Moreover, the evaluateddata object, based at least in part upon the structured patient data,may be utilized as a portion of the data abstraction. For example, theevaluated data object may be a result (e.g., an adherence event, aviolation event, a therapy result, a disease assessment, a therapyassessment, a therapy optimization and/or an adverse event (but not a bGvalue)) that can be associated with structured patient data and/orcasual patient data.

Referring to FIG. 21, data flow 1200 according embodiments of thepresent disclosure is depicted. The at least one memory may besegregated into a primary data store 1210 and a secondary data store1250. The primary data store 1210 may be utilized to store all of thecollected data 1202 collected, for example, by the collection device 24.The collected data 1202 may include patient data such as, for example,biomarker data, date-time stamps, completion flags, contextualized data,and the like. While the secondary data store 1250 may be utilized toduplicate and store a portion of the data stored in the primary datastore 1210. Such data may be duplicated by copying and/or by generatinga data abstraction. In a further embodiment, the data abstraction may bewritten to a tertiary data store (not depicted) and may be limited todata related to the administration of therapy, i.e., data commonlyaccessed by a physician to administer and/or alter the therapy of apatient.

In one embodiment, collected data 1202 includes structured patient datacollected by performing structured testing 1220, i.e., the structuredpatient data may be collected in accordance to a schedule of events 222(FIG. 5B). The structured patient data can be written to the secondarydata store 1250. The data flow 1200 may further include the process 1230of transforming a relevant portion of the structured patient data intoan evaluated data object (e.g., a classification, a disease assessment,a therapy assessment, or a therapy optimization), as is described ingreater detail above. After the evaluated data object is instantiated,the evaluated data object may be written automatically to the secondarydata store 1250 and associated with the primary data store 1210.Specifically, the evaluated data object may be written directly orindirectly to the primary data store 1210 depending on the type of dataabstraction utilized. In further embodiments, the evaluated data objectmay be written automatically to the primary data store 1210 andassociated with the secondary data store 1250.

Referring still to FIG. 21, data flow 1200 depicts the process 1240 ofgenerating a data abstraction. The data abstraction is a transformationof data stored in the secondary data store 1250 that is an indirectrepresentation of the data. The unique identifier may be a unique valuethat is stored with and/or associated with the relevant portion of thedata. The unique value (e.g., an incremental count or metadata) may becapable of identifying data instances such the data instance may beretrieved from the primary data store 1210 by, for example, querying forthe unique value. The unique value may also be the result of atransformation of the data instance into a compressed data, i.e.,transforming the data instance by a data compression algorithm such as,for example, a lossless data compression algorithm or a lossy datacompression algorithm.

In one embodiment, the data abstraction may be associated with anassociated portion of the structured patient data. Specifically, thedata abstraction may be written to the secondary data store 1250, i.e.,appended to a data structure of the associated portion of the structuredpatient data or stored as a separate data structure with linkinginformation. When a data abstraction is generated, data from thesecondary data store 1250 may be transformed into a unique identifier, apointer or a combination thereof, as is described hereinabove. The dataabstraction may be a unique identifier indicative of a correspondingportion of the casual patient data written in the primary data store1210. For example, the corresponding portion of the casual patient datamay be duplicate data or sufficient data to reconstruct the relevantportion of the structured patient data. Additionally, the dataabstraction may be a relevant portion pointer indicative of acorresponding address of the primary data store. The correspondingaddress of the primary data store may be a data pointer that isassociated with an address of the primary data store. Duplicate dataand/or data the can be used to reconstruct the relevant portion of thestructured patient data may be written at the address of the primarydata store. Furthermore, the data abstraction may include reconstructioninformation that enables the program instructions cause the processor toreconstruct a reconstructed structured patient data corresponding tostructured patient data written to the secondary data store 1250. Thus,the structured patient data may be generated even if the structuredpatient data is lost, corrupted, deleted and/or overwritten.

In some embodiments, only a subset of the structured patient isassociated with a data abstraction. The remainder of the structuredpatient may be classified as a non-relevant portion of the structuredpatient data which is not the associated portion of the structuredpatient data. When new structured patient data is written to thesecondary data store 1250, the non-relevant portion of the structuredpatient data may automatically be overwritten to reduce the amount ofphysical memory required for storage. In another embodiment, thenon-relevant portion of the structured patient data may be compressedwith a data compression algorithm. In a further embodiment, the datastores may be reset such that all of the structured patient data becomesunassociated (i.e., converted to the non-relevant portion). Thus, if,for example, a health care professional were to retrieve data from theat least one memory, the data stores could be reset such that all of theretrieved data may be overwritten and/or deleted.

As is noted above, a data abstraction may be associated with at leastone instance of the casual patient data (e.g., bG value) written in theprimary data store 1210. The data abstraction may be written directly orindirectly to the primary data store, i.e., appended to a data structureof the casual patient data or stored as a separate data structure. Forexample, a data structure may be created that appends the dataabstraction to the casual patient data or through the use of an indexingsystem that correlates the data abstraction to the casual patient data.In one embodiment, the data abstraction may be an evaluated data objectpointer indicative of an address of the secondary data store. Theaddress may correspond to the memory location of the evaluated dataobject stored in the secondary data store. Moreover, data abstractionmay be associated dynamically (e.g., the data abstraction may beassociated with a primary sample after it has been promoted from being asecondary sample).

Data redundancy may be supported by dynamic and/or static allocation ofthe at least one memory. Memory allocation relates to the physicalquantity of memory allocated to each of the logical segregated datastores of the at least one memory. Thus, the physical quantity of the atleast one memory (e.g., bytes of RAM) may remain fixed or may be variedas the logical allocation of the at least one memory is altered, i.e.,increased or decreased. In one embodiment, the quantity of logicallyaccessible segments available to the primary data store 1210 and/or thequantity of logically accessible segments available to the secondarydata store 1250 may be allocated prior to initiating the schedule ofevents. In another embodiment, the quantity of logically accessiblesegments available to the primary data store 1210 and/or the quantity oflogically accessible segments available to the secondary data store 1250may be allocated after an event of the schedule of events ends.

An error checking algorithm may be executed to check for errors on eachdata store of the at least one memory prior to and/or after writing datato the data store. In one embodiment, an error checking algorithm isapplied the primary data store after writing to the primary data storeand a different error checking algorithm is applied to the secondarydata store after writing to the secondary data store. Error checkingalgorithms are configured to transform data to a substantially uniqueresult. For example, all of the data written in a data store may becombined (e.g., summed) with the combined value transformed into a hashvalue (i.e., checksum). The hash value may then be stored at a knownlocation and later accessed by a processor. After writing data to thedata store, the error checking algorithm may be executed to generate anew hash value. If the new hash value matches the stored hash value, thedata store may be free of errors (e.g., data corruption, transmissionerrors and the like). Non-limiting examples of error checking algorithmsinclude cyclic redundancy checks, checksums, hash functions, and thelike.

Referring now to FIG. 22, an embodiment of logical segregation 1300 isschematically depicted. As is described in greater detail herein, dataabstractions such as unique identifiers may be utilized to link dataacross a plurality of data stores. In one embodiment, the logicalsegregation system comprises a primary data store 1210 and a secondarydata store 1250 on at least one memory. The primary data store 1310comprises casual patient data 1312 and associated casual patient data1314. The secondary data store 1250 comprises at least one structuredtesting instance 1320. Each structured testing instance 1320 comprisesat least one sample group which can be identified sequentially (e.g.,date, time and/or memory location).

A structured testing instance 1320 may include a running sample groupdata vector 1322, which may include at least one running sample instancedata vector 1324. Such running data is maintained as required for thegeneration of an evaluated data object 1332 (e.g., until all of therunning data can be classified as a primary sample or a secondarysample). Therefore, each running sample instance data vector 1324 mayinclude sufficient data (e.g., recommended insulin administrationamount, the confirmed insulin administration amount, whether the useradministered their insulin, whether the user affirmed they were fastingin the morning, and/or the associated bG value) for the generation of anevaluated data object 1332 (e.g., a disease status assessment, a therapyassessment, a therapy optimization, or combinations thereof). Once theevaluated data object has been instantiated, the running data may betransformed into historical data 1330.

The historical data 1330 may include data from prior sample groupswithin the structured testing instance 1320. The amount of physicalmemory required to store historical data 1330 may be reduced by storingthe evaluated data object 1332 rather than the full sample instance datavector. Additional reductions to the amount of physical memory requiredto store historical data 1330 can be achieved by omitting data basedupon the evaluated data object 1332, e.g., by omitting secondary samplesand tertiary samples. Moreover, historical data 1330 may be compressedvia compression algorithms to reduce the amount of physical memoryrequired for storage.

In order to link the primary data store 1210 and the secondary datastore 1250, each of the evaluated data objects 1332 may be concatenatedwith a data abstraction. For example, the data abstraction may include aunique identifier, a sample group number, an indication whether thesample is primary, secondary, or tertiary, and an indication whether thesample group was accepted. In the embodiment depicted in FIG. 22, afirst data abstraction 1340, a second data abstraction 1342, and a thirddata abstraction 1344 are concatenated to an evaluated data object 1332.When each of the first data abstraction 1340, the second dataabstraction 1342, and the third data abstraction 1344 is distinct, oneof the data abstractions may be concatenated to the associated casualpatient data 1314 to link the associated casual patient data 1314 of theprimary data store 1210 and the evaluated data object 1332 of thesecondary data store 1250. Specifically, in the depicted embodiment, thesecond data abstraction 1342 links historical data 1330 to theassociated casual patient data 1314.

It should now be understood that by providing linked data stores, theembodiments described herein offer redundant data segregation andefficient use of memory. Comprehensive bG sample information may bestored in a first data store, while a reduced amount of data includingtherapy change information such as dosage recommendation, average bGvalue, and new recommended insulin dosage may be stored in a second datastore. Thus, a user may scroll through all acquired bG information, inorder of acquisition, on a device while a reduced amount of data isduplicated in a second data store. Moreover, the full data set may bere-created the data set for a health care professional reviewing thetherapy change information. As a result, patient data that alterstherapy administered to a patient may have increased reliability instorage and retrieval.

Thus, by the above disclosure embodiments concerning a system and methodmanaging the execution, data collection, and data analysis of collectionprocedures running simultaneously on a meter are disclosed. One skilledin the art will appreciate that the teachings can be practiced withembodiments other than those disclosed. The disclosed embodiments arepresented for purposes of illustration and not limitation, and theinvention is only limited by the claims that follow.

What is claimed is:
 1. A collection device for performing a structuredcollection procedure, the collection device comprising: a processorcommunicably coupled to at least one memory; and program instructionswhich when executed by the processor cause the processor to: initiate aschedule of events of the structured collection procedure upon one ormore entry criterions being met; segregate the at least one memory intoa primary data store and a secondary data store; write structuredpatient data collected in accordance to the schedule of events to thesecondary data store of the at least one memory; transform a relevantportion of the structured patient data into an evaluated data object;generate a data abstraction based at least in part upon the evaluateddata object; and link the primary data store and the secondary datastore with the data abstraction; wherein the at least one memorycomprises dynamic and static allocation such that the quantity of the atleast one memory can remain fixed, can be increased, and can bedecreased; and the program instructions when executed by the processorcause the processor to: mark the collection procedure as complete ifthere are no exceptions; signify that appropriate data has not beencollected if there are the exceptions; resolve any of the exceptionsthat have arisen at the time of execution of the collection procedure;and change the structured collection procedure by switching to asecondary schedule of events that is easier for a patient to perform. 2.The collection device of claim 1, wherein the relevant portion of thestructured patient data comprises a primary sample, wherein the primarysample is promoted from a secondary sample.
 3. The collection device ofclaim 1, wherein the program instructions cause the processor to writecasual patient data collected by the collection device to the primarydata store of the at least one memory.
 4. The collection device of claim3, wherein the structured patient data is a subset of the casual patientdata, and the program instructions cause the processor to: write thedata abstraction to the primary data store and/or the secondary datastore; associate the data abstraction with an associated portion of thestructured patient data, wherein a non-relevant portion of thestructured patient data is not the associated portion of the structuredpatient data; and omit the non-relevant portion of the structuredpatient data from the secondary data store of the at least one memory.5. The collection device of claim 4, wherein the program instructionscause the processor to compress the associated portion of the structuredpatient data with a data compression algorithm.
 6. The collection deviceof claim 3, wherein the program instructions cause the processor towrite automatically the evaluated data object to the secondary datastore.
 7. The collection device of claim 3, wherein the programinstructions cause the processor to: associate the data abstraction withan associated portion of the structured patient data; and write the dataabstraction to the secondary data store.
 8. The collection device ofclaim 7, wherein the data abstraction is a unique identifier indicativeof a corresponding portion of the casual patient data written in theprimary data store, wherein the corresponding portion of the casualpatient data corresponds to the relevant portion of the structuredpatient data.
 9. The collection device of claim 7, wherein the dataabstraction is a relevant portion pointer indicative of a correspondingaddress of the primary data store, wherein the corresponding address ofthe primary data store corresponds to the relevant portion of thestructured patient data.
 10. The collection device of claim 7, whereinthe data abstraction comprises reconstruction information indicative ofan address of the primary data store corresponding to the casual patientdata and the program instructions cause the processor to reconstruct areconstructed structured patient data corresponding to the structuredpatient data written to the secondary data store.
 11. The collectiondevice of claim 7, wherein a non-relevant portion of the structuredpatient data is not the associated portion of the structured patientdata, and the program instructions cause the processor to compress thenon-relevant portion of the structured patient data with a datacompression algorithm.
 12. The collection device of claim 6, wherein theprogram instructions cause the processor to: associate the dataabstraction with at least one instance of the casual patient data; andwrite the data abstraction to the primary data store.
 13. The collectiondevice of claim 12, wherein the data abstraction is an evaluated dataobject pointer indicative of an address of the secondary data storecorresponding to the evaluated data object stored in the secondary datastore.
 14. The collection device of claim 1 wherein: the primary datastore comprises a first quantity of logically accessible segments; thesecondary data store comprises a second quantity of logically accessiblesegments; and the first quantity of logically accessible segments and/orthe second quantity of logically accessible segments are allocated priorto initiating the schedule of events.
 15. The collection device of claim1 wherein: the primary data store comprises a first quantity oflogically accessible segments; the secondary data store comprises asecond quantity of logically accessible segments; the programinstructions cause the processor to increase the first quantity oflogically accessible segments and/or the second quantity of logicallyaccessible segments after an event of the schedule of events ends. 16.The collection device of claim 1, wherein the evaluated data object is atherapy result, a disease assessment, a therapy assessment, a therapyoptimization and/or an adverse event.
 17. The collection device of claim3, wherein the casual patient data comprises biomarker data, a date-timestamp, a completion flag, or contextualized data.
 18. The collectiondevice of claim 1, wherein the at least one memory comprises a mainmemory disposed within the collection device.
 19. The collection deviceof claim 1, wherein the at least one memory comprises a computerreadable medium and/or a network database.
 20. The collection device ofclaim 1, wherein the program instructions cause the processor to: applyan error checking algorithm to the primary data store after writing tothe primary data store; and apply a different error checking algorithmto the secondary data store after writing to the secondary data store.21. The collection device of claim 20, wherein each of the errorchecking algorithm and the different error checking algorithm is acyclic redundancy check.
 22. A collection device for performing astructured collection procedure, the collection device comprising: aprocessor communicably coupled to at least one memory wherein the atleast one memory comprises dynamic and static allocation such that thequantity of the at least one memory can remain fixed, can be increased,and can be decreased; and program instructions which when executed bythe processor cause the processor to: initiate a schedule of events ofthe structured collection procedure upon one or more entry criterionsbeing met; segregate the at least one memory into a primary data storeand a secondary data store; write casual patient data collected by thecollection device to the primary data store of the at least one memory;write structured patient data collected in accordance to the schedule ofevents to the secondary data store of the at least one memory; transforma relevant portion of the structured patient data into an evaluated dataobject, the evaluated data object is a therapy result, a diseaseassessment, a therapy assessment, a therapy optimization and/or anadverse event; write automatically the evaluated data object to thesecondary data store; apply an error checking algorithm to the primarydata store after writing to the primary data store; apply a differenterror checking algorithm to the secondary data store after writing tothe secondary data store; link the primary data store and the secondarydata store with the evaluated data object; and mark the collectionprocedure as complete if there are no exceptions; signify thatappropriate data has not been collected if there are the exceptions;resolve any of the exceptions that have arisen at the time of executionof the collection procedure; and change the structured collectionprocedure by switching to a secondary schedule of events that is easierfor a patient to perform.
 23. A method for managing data collected froma structured collection procedure comprising: providing at least onememory comprising dynamic and static allocation such that the quantityof the at least one memory can remain fixed, can be increased, and canbe decreased; initiating a schedule of events of the structuredcollection procedure upon one or more entry criterions being met;segregating the at least one memory into a primary data store and asecondary data store; writing casual patient data collected by acollection device to the primary data store of the at least one memory;writing structured patient data collected in accordance to the scheduleof events to the secondary data store of the at least one memory;transforming a relevant portion of the structured patient data into anevaluated data object; generating a data abstraction based at least inpart upon the evaluated data object; linking the primary data store andthe secondary data store with the data abstraction; and if there are noexceptions, marking the collection procedure as complete; if there arethe exceptions, signifying that appropriate data has not been collected;resolving any of the exceptions that have arisen at the time ofexecution of the collection procedure; and changing the structuredcollection procedure by switching to a secondary schedule of events thatis easier for a patient to perform.
 24. The method of claim 23, furthercomprising writing automatically the evaluated data object to thesecondary data store.
 25. The method of claim 24, further comprising:associating the data abstraction with an associated portion of thestructured patient data; and writing the data abstraction to thesecondary data store.
 26. The method of claim 25, further comprisingresetting automatically the secondary data store wherein the associatedportion of the structured patient data is unassociated.
 27. The methodof claim 24, further comprising: associating the data abstraction withat least one instance of the casual patient data; and writing the dataabstraction to the primary data store.